{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><b>Introduction</b></h3>\n",
    "<p> The goal of this project is to take the publically available Beijing weather data from 2013 to 2017 and apply machine learning techniques to see if we can predict the amount of PM2.5 concentration in the air given other environmental features. This is a project I am working on during my free time applying some of the machine learning algorithms I have learned. I hope to come up with a predictive model with a high accuracy and a very low Root Mean Square Error (RMSE).</p>\n",
    "<br>\n",
    "<h5><b>Dataset Information</b></h5>\n",
    "<p>This data set includes hourly air pollutants data from 12 nationally-controlled air-quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017. Missing data are denoted as NA.</p>\n",
    "<br>\n",
    "<h5><b>Project workflow</b></h5>\n",
    "<p>This project utilizes the Supervised Machine Learning algorithms from python’s Scikit-learn library. The model we hope to succeed in training is a regression model and below are the steps we will go through in this jupyter notebook for this project:</p>\n",
    "<ul>\n",
    "    <li>Import the neccessary libraries and loading the data</li>\n",
    "    <li>Data preprocessing</li>\n",
    "    <li>Exploratory Data Ananlysis</li>\n",
    "    <li>Model training and Evaluation</li>\n",
    "    <li>Saving the model</li>\n",
    "</ul>\n",
    "<br>\n",
    "<h5><b>Import libraries and loading data</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import statsmodels.formula.api as formula\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "import statsmodels as sm\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV,RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Aotizhongxin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  year  month  day  hour  PM2.5  PM10   SO2   NO2     CO    O3  TEMP  \\\n",
       "0   1  2013      3    1     0    4.0   4.0   4.0   7.0  300.0  77.0  -0.7   \n",
       "1   2  2013      3    1     1    8.0   8.0   4.0   7.0  300.0  77.0  -1.1   \n",
       "2   3  2013      3    1     2    7.0   7.0   5.0  10.0  300.0  73.0  -1.1   \n",
       "3   4  2013      3    1     3    6.0   6.0  11.0  11.0  300.0  72.0  -1.4   \n",
       "4   5  2013      3    1     4    3.0   3.0  12.0  12.0  300.0  72.0  -2.0   \n",
       "\n",
       "     PRES  DEWP  RAIN   wd  WSPM       station  \n",
       "0  1023.0 -18.8   0.0  NNW   4.4  Aotizhongxin  \n",
       "1  1023.2 -18.2   0.0    N   4.7  Aotizhongxin  \n",
       "2  1023.5 -18.2   0.0  NNW   5.6  Aotizhongxin  \n",
       "3  1024.5 -19.4   0.0   NW   3.1  Aotizhongxin  \n",
       "4  1025.2 -19.5   0.0    N   2.0  Aotizhongxin  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the name of the csv file \n",
    "#file = 'https://raw.githubusercontent.com/prince381/air-pollution/master/data/PRSA_Data_Aotizhongxin_20130301-20170228.csv' \n",
    "file = r'C:\\Users\\DELL\\Desktop\\EDUCATE\\DATA CSV\\PRSA_Data_20130301-20170228\\data\\PRSA_Data_Aotizhongxin_20130301-20170228.csv'\n",
    "\n",
    "# read the csv file into a pandas DataFrame using the pd.read_csv()\n",
    "data = pd.read_csv(file)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><b>Data Preprocessing</b></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35064 entries, 0 to 35063\n",
      "Data columns (total 16 columns):\n",
      "year     35064 non-null int64\n",
      "month    35064 non-null int64\n",
      "day      35064 non-null int64\n",
      "hour     35064 non-null int64\n",
      "PM2.5    34139 non-null float64\n",
      "PM10     34346 non-null float64\n",
      "SO2      34129 non-null float64\n",
      "NO2      34041 non-null float64\n",
      "CO       33288 non-null float64\n",
      "O3       33345 non-null float64\n",
      "TEMP     35044 non-null float64\n",
      "PRES     35044 non-null float64\n",
      "DEWP     35044 non-null float64\n",
      "RAIN     35044 non-null float64\n",
      "wd       34983 non-null object\n",
      "WSPM     35050 non-null float64\n",
      "dtypes: float64(11), int64(4), object(1)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# drop the unwanted columns/features\n",
    "cols_to_drop = ['No','station']\n",
    "data = data.drop(cols_to_drop,axis=1)\n",
    "# print out the info of the data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Are there any duplicated values in our data ? : False\n",
      "\n",
      "The total number of null values in each colum:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "year        0\n",
       "month       0\n",
       "day         0\n",
       "hour        0\n",
       "PM2.5     925\n",
       "PM10      718\n",
       "SO2       935\n",
       "NO2      1023\n",
       "CO       1776\n",
       "O3       1719\n",
       "TEMP       20\n",
       "PRES       20\n",
       "DEWP       20\n",
       "RAIN       20\n",
       "wd         81\n",
       "WSPM       14\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check for duplicated values and null values\n",
    "print('Are there any duplicated values in our data ? : {}\\n'.format(data.duplicated().any()))\n",
    "print('The total number of null values in each colum:')\n",
    "display(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NE\n",
       "dtype: object"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the most appearing wind direction value\n",
    "data.wd.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year     False\n",
       "month    False\n",
       "day      False\n",
       "hour     False\n",
       "PM2.5    False\n",
       "PM10     False\n",
       "SO2      False\n",
       "NO2      False\n",
       "CO       False\n",
       "O3       False\n",
       "TEMP     False\n",
       "PRES     False\n",
       "DEWP     False\n",
       "RAIN     False\n",
       "wd       False\n",
       "WSPM     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill in the missing values with the mean of the particular column\n",
    "data.fillna(value=data.mean(),inplace=True)\n",
    "# replace the missing values for the wind direction with the modal value\n",
    "data.wd.fillna(value='NE',inplace=True)\n",
    "# let's check the data again if there are any missing values\n",
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>wd</th>\n",
       "      <th>WSPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-01 00:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>1023.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-01 01:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.2</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-01 02:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>-18.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-01 03:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>1024.5</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-01 04:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1025.2</td>\n",
       "      <td>-19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>N</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  year  month  day  hour  PM2.5  PM10   SO2   NO2     CO  \\\n",
       "0 2013-03-01 00:00:00  2013      3    1     0    4.0   4.0   4.0   7.0  300.0   \n",
       "1 2013-03-01 01:00:00  2013      3    1     1    8.0   8.0   4.0   7.0  300.0   \n",
       "2 2013-03-01 02:00:00  2013      3    1     2    7.0   7.0   5.0  10.0  300.0   \n",
       "3 2013-03-01 03:00:00  2013      3    1     3    6.0   6.0  11.0  11.0  300.0   \n",
       "4 2013-03-01 04:00:00  2013      3    1     4    3.0   3.0  12.0  12.0  300.0   \n",
       "\n",
       "     O3  TEMP    PRES  DEWP  RAIN   wd  WSPM  \n",
       "0  77.0  -0.7  1023.0 -18.8   0.0  NNW   4.4  \n",
       "1  77.0  -1.1  1023.2 -18.2   0.0    N   4.7  \n",
       "2  73.0  -1.1  1023.5 -18.2   0.0  NNW   5.6  \n",
       "3  72.0  -1.4  1024.5 -19.4   0.0   NW   3.1  \n",
       "4  72.0  -2.0  1025.2 -19.5   0.0    N   2.0  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a datetime column using the year,month,day and hour columns.\n",
    "years = data['year'].values\n",
    "months = data['month'].values\n",
    "days = data['day'].values\n",
    "hours = data['hour'].values\n",
    "full_date = []\n",
    "\n",
    "for i in range(data.shape[0]):\n",
    "    date_time = str(years[i])+'-'+str(months[i])+'-'+str(days[i])+' '+str(hours[i])+':'+str(0)\n",
    "    full_date.append(date_time)\n",
    "\n",
    "dates = pd.to_datetime(full_date)\n",
    "dates = pd.DataFrame(dates,columns=['date'])\n",
    "data = pd.concat([dates,data],axis=1)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><b>Exploratory Data Analysis</b></h3>\n",
    "<p>Before we start fitting a machine learning model on the data, we need to know much about the data by performing an Exploratory Data Analysis to gain insight from it. <abbr title=\"Exploratory Data Analysis\">EDA</abbr> is simply describing the data by means of visualization. It involves asking questions about the data and answering them with the help of charts/graphs (graphical representation of the data). In this process, we will try to study the behavoir of the amount of pollutant (PM2.5 concentration) in the air and the relationship between other features. Below are some of the questions we will try to answer by analyzing the data, to know more about our dependent and independent variables:</p>\n",
    "<ul>\n",
    "    <li>what pattern does the amount of PM2.5 concentration in the air recorded in an hour follow\n",
    "        for a daily time period ?</li>\n",
    "    <li>In which month does the amount of PM2.5 contained in the air rises ?</li>\n",
    "    <li>At what time of the day do we expect the amount of PM2.5 concentration in the\n",
    "        air to be high ?</li>\n",
    "    <li>In which direction does polluted air/wind mostly move ?</li>\n",
    "    <li>How do the other environmental factors affect the amount of PM2.5 concentration\n",
    "        in the air ?</li>\n",
    "</ul>\n",
    "<p>We now have our questions so let's just dive into our data and start finding and interpreting some results. But since we are going to take averages of the dependent variable, we shoul know the distribtution of the data before we do take averages.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2adLEaC/KpKSkICsrq1SrnO6RaHJysjDNz88PIpFI+Ozl5VXhSz9JSUkIDQ0V/ggDxfvr4+ODpKSkJ657YGCg8H+pVApPT0/hMeiZM2dgY2ODDh06QK1WC8FVcHAwTp8+XWG5bdq00dvPxo0bC+Xqjsfjx6tPnz7IyspCSkpKlfahovPv6uqK4OBg7N+/HwDw6NEjHD9+HK+++mqFZT7zzDNCYA0UH4umTZuiadOmwrGwtrZGu3bthGNx/vx5dOjQQQisASA0NBSHDh2Ci4uLwefy8ZzYc+fO4YUXXtBr3Q0NDRUCN0Prd+7cOXTr1k3vvPTu3bvC46CzZcsWtG7dGq1bt4afnx969+6No0ePYvr06ejVq1ep5cPCwnDx4kU8ePAAQHGrdVBQUKW5vl9++SU+//xzjBo1qtwfTAAwY8YMbNmyBe+88w7at2+PqKgoLF68GKdPn9b7vpV0/vx5PPfcc0JgDQCtWrXCsWPH8PzzzyMpKalU2oqrqys6d+6sF2BJpVL4+voKn728vABASOs4f/48QkJC9M7PsGHDsGPHDkgkEpw5cwZdu3aFWCwWzlXbtm1hb29fqkelxz1+rcfGxuL1119HWloaTp06hc2bN+PcuXMoLCyssByd33//HSqVqtR3sW/fvlCpVPj999+FaW3atNFbxtPTs8ovLJYMksViMTw8PKr10mPJHqCcnZ1hZ2end9+q7LvwOCsrKyQkJCAkJASpqan49ddfsX79eiG3vyLM+a+f2HJNtYZSqUROTg48PT1LzROLxdiwYQPi4uJw9OhR7NixA9bW1hg9ejTeffddvYDgcZX9wXZzcytznYcPH1Z9J8qge9T4+Hbs7e1hZWWl96j68bQVkUhUYT+uubm5Zdbfzc2t0lxtQ1hbW+t9FovFQn0ePXoEuVyuF0jolAw8y1LRfubk5EAqlcLZ2VlvmQYNGgAofnxva2tr8D5Udv5fe+01TJ48GQ8ePMDPP/9cqneLsjx+zB89eoSbN2+W+Vhdl/ebk5MjPKEoi6Hn8vFlcnNz4eLiojdNIpHo7bch9SurHN0xr8wrr7yC0aNHAyi+RhwcHNC4cWOIxWW33wQFBcHZ2RlHjhxBVFQUDh06hDfffLPc8rVaLZYvX44NGzZg6NCheO+99yqsz4svvlhqmu7p1dWrV9G+fftS83Nycso8/jq5ubll9rTj5uaGGzduCJ8tLS319lv3f41GY9B2Hj16hMTERCQmJpaal56eXu56QOlr/dy5c1i0aBGuXbsGBwcHtGrVClZWVhWWUZLu3vX4daCrf0X3rpL3CkNVdL8xVjmGfBfKcvToUcTExOB///sfXFxc4OvrC2tra+G8lsXW1rZK9yp6ejC4plojOTkZarUa7dq1K3N+w4YNsWzZMmg0Gly4cAHbtm3DZ599hueee67C/NjKlHwhRScjI0PoKkwkEpW6gRYUFBhcvi5IzMzMLLVdpVJZKoisCicnp1LlAsX1r+kWEwcHB7i5ueGLL74warlOTk5Qq9V49OiR3rHRpQs5OzsLP6YePy/VaeXq0aMHHB0dceTIEfz0008IDw+vUgACFB+Lli1bYunSpaXm6Vqi7e3tS/UdXFhYiFOnTiEgIKDa59LZ2blUa75Wq9XLHzWkfs7OzqW2X/Jly4q4urpW6dG3RCJBaGgoDh8+jJCQEFy/fr3cVnKNRoNZs2Zhz549GD9+vJCeUB61Wo09e/agZcuWekG2QqEAgFI/IHQcHBzKfFfgl19+QevWreHk5FRmylpGRkaVvsNlXQePHj3C5cuXERgYCHt7e4SGhpb5Y6O8upclLy8P48ePR2BgIOLi4tC0aVMAwIoVK4T848ro9isjI0Ov0aPkd7GuMeS78Ljbt29j8uTJeO211/Dtt98KTyMmT55c5SdpVD8wLYRqBa1Wiy+//BLOzs5l/pG9evUqgoODcfnyZYjFYgQGBmLp0qWQSqXC2/zltZJVJisrC5cvXxY+X758GampqUL/uHZ2dsjMzNQL5B4fiKWibTdv3hwuLi6lerbQDYRRMvWiqtq1a4ejR4/qPeZNSUnBn3/+WaVyq3Ps2rVrh6ysLNja2sLPz0/4t3fvXuzZs6fK5ZUsF0CZx8vNzQ3NmjUTUitKPl1IS0urVu8WlpaWeOWVV7B371789ttvlaaElCUwMBCpqanw9vYWjoOvry82bNiAn3/+WVgmKSlJ74fZqVOnMHbsWGRmZlb7XHbs2BHXr1/X67Xi1KlTeuUYUr+OHTvip59+0uvhobyXyYyhd+/eOHv2LLZu3VphSsjy5cuxZ88ezJ49u9LAGihOy4iLi9Pr9QYADh8+DAsLi3K7SwsICMD169dx9+5dYVpKSgrGjh2Lq1evol27djhz5oxeYJyVlYVTp05V6bsWEBCA48eP691PDhw4ILyc3a5dO9y8eRO+vr7CuWrYsCH+/e9/4/r16wAM+77evHkTOTk5GDFihBBYazQanDx5Uq81uKKy/Pz8YGFhUeZ3USqVmr1rz+rctwz5Ljzuv//9L1QqFcaOHSsE1jKZDGfPnuUIkVQmtlyTyeXm5uLChQsAiluZ0tLSsG3bNiQlJWHlypV6Oak6zz33HOzs7DBr1ixMnDgRTk5O2LVrF0QikfCWuKOjIx48eIATJ06UmapQHktLS0ybNg0zZsyASqXCypUr0bJlS4SFhQEAQkJC8M0332Dx4sXo06cPTp8+jR9//FGvDEdHR8jlcvz444+l/uBIJBJMnDgRS5YsgZOTE0JDQ3Ht2jXExcUhPDy8zME0DDV+/HgMGTIEY8aMwVtvvYW8vDzExsbC29vboJcxS9YfKO4usGvXrgat06NHD/j5+WHs2LGYOHEiGjZsiMOHD2Pz5s1YvHhxtfYHgHDsly9fjoKCAvj4+ODo0aPYv38/FixYALFYDCcnJ7Rp0wYJCQlo2LAhJBIJ1qxZU+2BL1577TV899138Pb2LjNloDKvv/46vvnmG4waNQpjx46Fs7MzEhMTcfjwYfzrX/8CUPyy2ffff49x48Zh1KhRkMlkWLlyJXr37o3mzZtX+1wOGDAACQkJQquubkTDkqk5htRv/PjxGDhwIN555x2h14zNmzdX+VgYqkuXLrCzs8PGjRsxb968Mpe5fPkyNm3ahK5duyIgIEC4bwDFgZXuu3bjxg0UFhYKLdXjx4/HggULsHTpUvTs2ROXLl3CZ599hqioKHh7e5e5rYEDB2LDhg0YP348Jk6cCIlEgk8++QT+/v7o1KkTnn32WXz//fcYNWoUoqOjodVqsXbtWlhaWlZptMTx48cjMjISkyZNwhtvvIEHDx4gNjYWw4YNg729PaKjozFkyBBMnjwZAwcORGFhIeLj43H//n1h/yq63+i0aNECdnZ2iI+Ph0ajgUKhwJYtW3D16lUhDUskElV433R1dUVUVBTWrVsHiUSCDh06ICkpCevWrcPIkSPh5ORk8H7XBEOOw+MM+S48rlWrVpBIJPj444/x5ptvIjs7GwkJCcjIyCi3tZvqN7Zck8mdO3cOgwcPxuDBg/HWW29h5cqVcHR0xH/+859y0zukUim++uorNG3aFIsWLcK4ceNw8+ZNfPHFF8JLPIMHD4abmxvGjRuHEydOGFwfb29vjBw5EosXL8bcuXPh7++PhIQE4aYZEhKCqVOn4ujRoxg7diyuXLmC5cuX65XRt29ftG7dGlOmTMHu3btLbWPYsGH48MMPcebMGYwfPx6bN28WRqR8Er6+vti4cSPUajUmT56MDz/8EO3bt8d//vOfMn+klKdz584IDg7GkiVLkJCQYNA6EokE69atQ9euXYUu4JKSkhATE/PEfbquXLkSkZGR2LBhAyZMmIBz587h448/RmRkpLBMTEwMmjZtihkzZmDZsmUYMmRItYe3btu2LZycnNC/f/8K8/fLY29vj82bN6NFixZYtGgRoqOjce/ePcTHxws9HTRp0gTffvstLCwsMHXqVCxfvhy9evUSrqXqnksrKyts3LgRzz77LGbPni1041gy8DGkfs8//zwSEhKQnZ2Nd999V/gxU1MsLCzQo0cPaDSaclNCjh07Bq1WixMnTgj3DN2/kgHt4sWL9Xp5GDx4MGJiYoTvW2JiIqKjo8vtdQcoDtS+/fZbPPPMM5g9ezbmzp2Lli1b4vPPP4dUKkXDhg2xefNmeHh4YNasWZg7dy4aNWqE7777TmjNNETbtm2xbt06pKen45133sHatWsRFRUldMmouw6ys7MxadIkzJ07F56envjmm2+E1IzK7jdAcfpDXFwccnNzMWHCBHzwwQdwdnbGJ598Ao1GI7yMWNl9c+bMmZgyZQr27t2LcePG4YcffsB7772HGTNmGLzPNcWQ4/A4Q74Lj2vevDk++ugjXLt2DWPHjsXKlSvh6+uLhQsX4v79+3pdMRIBgEjLZxpEVM9dvHgRgwYNwqFDh6o88AgREVFJTAshonrr0qVL+Pnnn7F792689NJLDKyJiOiJMS2EiOotuVyO9evXw8nJ6YlGIyQiItJhWggRERERkZGw5ZqIiIiIyEgYXBMRGRkfCBIR1V8MromozvLx8cG6deuMUpZarUbnzp3RunVrvcFpquLBgwcYPXo0srOzAQCpqanw8fEpNQhHeWbPno1+/fpVa9vmYIzjrztGJf/5+fkhLCwMX375ZZV/qERFRWHcuHEAgDNnzsDHxweXLl0qNY+IqKawtxAiqrMSExPRqFEjo5T1yy+/oLCwEA0aNBAGe6mqkydP4tdffxU+e3h4IDEx8anthcSYx3/atGlCP+VyuRwXLlzAJ598AgAYO3asweUsXLhQGLmvdevWSExMFIaPLzmPiKimMLgmojqrvKGsq2P37t3o1KkTGjZsiO3bt2Ps2LHVGlCmJEtLS6PWsbYx5r41bdpUr7zOnTvjzp07+O6776oUXOsGlQKKBwwpWWbJeURENYU/4YmoziqZlhAXF4eIiAjs27cPYWFh8PPzw8CBA3Hu3LlKy8nNzcVPP/2Ebt26oX///rhz5w7OnDlTarmrV6/i7bffRlBQEIKCgjBz5kxkZGQAAHbu3In3338fQHFgGBcXVyot5PH0B92/qKgove1s2rQJPXr0gL+/P6KiopCSkqI3/8iRIxg4cCDatm2L7t27IzY2FiqVSpjfs2dPfPXVV1i4cCGCgoIQGBiIWbNmIT8/X1hGqVRi6dKl6Ny5MwIDAzF37lysWrUKPXv2BAD88MMP8PHxwX/+8x9hnf3795faH2Mc//I4ODiUmvbHH39gxIgRaNOmDTp16oQlS5ZALpcL8w1NC9HNS05OxpAhQ+Dn54fQ0FBs27at2vUlIgIYXBPRU+T27dv49NNPMXHiRMTFxUGpVGLy5MlQq9UVrrd//35otVqEh4ejTZs2aNasWakg68qVKxg8eDBUKhWWL1+OOXPmIDk5GcOGDYNMJsNLL72ECRMmAAC+/vprDBo0qNR2EhMT9f7phux+7bXXhGVSUlKwa9cuzJ07F8uXL8etW7f0hu3Wrefn54c1a9Zg2LBhSEhIEAJ7nS+++AK5ubnCcOj79+/H2rVrhflz5szBzp07MXHiRPz73//GnTt3sH79emH+K6+8gpdffhmxsbHIyspCVlYWli5dir59+yI8PNyoxx8ANBoN1Go11Go1ZDIZTpw4gd27d+PNN98Ulrlx4waGDRsGkUiE2NhYzJgxAwcOHMCUKVMqLb8806ZNE/K7X3zxRcybNw83btyodnlEREwLIaKnRkFBATZs2AB/f38AQFFREaKjo3H16lX4+vqWu55uhEZnZ2cAwKuvvorPP/8cOTk5cHJyAgDEx8fD1dUVX331FSwtLQEAvr6+6N+/P3bs2IGoqCg888wzAIpzfV1dXZGamqq3nZIpCnfu3ME333yDN954AxEREXrLrV27Fp6engCAtLQ0LF++HPn5+bC1tUVsbCz69u0rDHoTHBwMBwcHLFy4EG+//TZatmwJAPDy8sKqVasgEokQHByM3377DcePH8fMmTNx69Yt7Nu3DzExMcK2O3XqhNDQUL16LFiwAH379kVsbCwKCgogkUiwYMECox9/AJg6dWqpaW3btkVkZKTwOT4+Hm5ubvjyyy8aYYPHAAAgAElEQVSFc9CsWTNERkYiKSkJHTp0qHAbZYmKisLIkSMBFJ+3I0eO4Pjx40whIaJqY8s1ET01pFKpXhDn5eUFAHppA4/766+/cP78efTq1Qu5ubnIzc1Fz549oVQqsWfPHmG5pKQkhIaGCkEdUJzD6+Pjg6SkpCrVs6CgANHR0WjcuDHmz5+vN69Ro0ZCYA0A3t7eAIpTV1JSUpCVlVWq5VjXw0hycrIwzc/PTy9n3MvLCzKZTNgXAOjVq5cw38bGBt27d9cr18PDA7NmzcK2bduwb98+LF26VPgBUpbKjr+uZVr3r2RPIDNmzMD27duxfft2bN68GfPnz8ft27cxYsQIIeXlzJkz6Nq1K8RisVBG27ZtYW9vj1OnTpVbr4qU/MHj6OgIW1tb4TgREVUHW66J6KlhaWmp1xuE7v8ajabcdXbt2gUAmDVrVql527ZtE/Khc3Nz4ebmVmoZNzc3vVzmymi1Wrz//vtIT0/Hjh079IJ1oDjILankPuTk5AjbLMne3h5WVlZ69Xi8HJFIJASz2dnZsLCwgKOjo94yDRo0KFXf3r17Y8mSJbCwsEBgYGCF+1bR8U9NTS3VMr5p0ybhx0OTJk3g5+cnzGvfvj1cXFwwbdo0HD16FOHh4Xj06JGQUvO49PT0CutWHmtra73PYrGY/ZQT0RNhcE1E9ZZWq8WePXvQs2dPvPXWW3rzTp06hbVr1+LixYvw9/eHk5MTMjMzS5WRkZEhdPVmiLVr1+Lw4cP44osv0Lhx4yrVV9dq/Hg9cnNzoVQqK2xVLsnDwwMqlQq5ubl6AXZWVlapZVesWAF7e3totVrExMQgJiamSnUuuc3t27frTWvevDkePXpU7jo+Pj4AilNogOIfEaGhoXp52DouLi7VqhcRkbExLYSI6q3k5GSkpqbijTfeQMeOHfX+jRw5EhYWFkJA2K5dOxw9ehSFhYXC+ikpKfjzzz+FFt3K+lD+5ZdfEBcXhwkTJpRKwTBE8+bN4eLiUmpQmgMHDgBApS3LOoGBgRCLxTh27JgwrbCwEP/3f/+nt9zp06exfft2zJo1C++99x527tyJkydPVrneQHGrtp+fn94/e3v7CtfR9fKhy2Vv164dbt68CV9fX6GMhg0b4t///jeuX79erXoRERkbW66JqN7atWsXHBwc0LVr11LznJycEBISgn379mH27NkYP348hgwZgjFjxuCtt95CXl4eYmNj4e3tjQEDBgCA0Ap85MiRUmXeuXMH06dPR6tWrfDyyy/j999/10s/MKTPaIlEgokTJ2LJkiVwcnJCaGgorl27hri4OISHh+OFF14waL+bNm2K/v37Y+nSpZDJZPD29samTZuQnp4uDAojl8sxf/58BAUF4V//+hcAYPv27Zg/fz727t0LW1tbg7ZlqL/++gsXLlwAUPxE4c8//8SqVavQrFkzoXvA6OhoDBkyBJMnT8bAgQNRWFiI+Ph43L9/Hy+++KJR60NEVF0MromoXlIqlTh06BB69uxZKu9Zp3///jh69Ch++OEHDBw4EBs3bsSqVaswefJk4QXAmTNnCi2wnTt3RnBwMJYsWYI33ngDo0aNEspKTk5GXl4eLl++rNf1ns61a9cMqvewYcNgbW2NhIQEbNu2DR4eHhg5ciSio6OrtP+LFi2CtbU1YmNjoVar0a9fP4SHhwvd0H3yySe4f/8+Pv/8c2GdhQsX4rXXXsPq1asxd+7cKm2vMqtWrRL+L5FI4OrqivDwcERHR+v1zrJx40bExsZi0qRJsLKyQmBgIFasWKH3EigRkTmJtHxzg4ioXsnKysKJEyfQo0cPvdSMIUOGoEGDBlizZo0Za2c8x48fx5gxY3DgwIEq5cUTET0JtlwTEdUz1tbWWLx4MQ4ePIghQ4ZAKpXihx9+wIULF/QGkqnLfv75Z+zcuRMWFhZs1SYik2LLNRFRPXTx4kWsXr0af/zxB1QqFXx8fDBhwgS89NJL5q6aUfTs2RMymQzjx48v1RMMEVFNYnBNRERERGQk7IqPiIiIiMhIGFwTERERERkJg2siIiIiIiOpk72FZGcXQKOpW6nibm72yMzMN3c1yEx4/onXQP3G80+8BuoesVgEFxe7Kq9XJ4NrjUZb54JrAHWyzmQ8PP/Ea6B+4/knXgP1A9NCiIiIiIiMhME1EREREZGRMLgmIiIiIjISBtdEREREREbC4JqIiIiIyEgYXBMRERERGQmDayIiIiIiI2FwTURERERkJAyuiYiIiIiMpE6O0FiXqTWAUqU2aFkrCymk/PlDREREVGcwuDYxpUqNpCtpBi3boZUnpFY8RURERER1BdtFiYiIiIiMhME1EREREZGRMLgmIiIiIjISBtdEREREREbC4JqIiIiIyEgYXBMRERERGQmDayIiIiIiI2FwTURERERkJAyuiYiIiIiMhME1EREREZGRGBRc7927F3369EHv3r2xefPmUvOvXLmCiIgIhIWFYe7cuVCr1XrzY2NjERcXJ3xOSUlBZGQkXn31VQwePBhXrlx5wt0gIiIiIjK/SoPrtLQ0rF69Glu2bMGuXbuQmJiIGzdu6C0zc+ZMLFiwAIcOHYJWq8XWrVsBAHl5eZgzZw7Wr1+vt/y8efMwZswY7N69G1OmTMGsWbOMuEtEREREROZRaXB98uRJdOrUCc7OzrC1tUVYWBgOHjwozL979y4UCgXatm0LAIiIiBDmHz16FM2aNcPIkSP1yhw0aBC6desGAPDx8cH9+/eNtkNEREREROZSaXD98OFDuLu7C589PDyQlpZW7nx3d3dh/oABAzB27FhIJBK9MiMiIoRpn376KXr16vVke0FEREREVAtIK1tAo9FAJBIJn7Vard7nyuaXR6vVYsWKFfj999+xadOmKlXazc2+SsvXFu7uDtBmyeBgb23Q8ra2VnB3ta3hWpGpuLs7mLsKZGa8Buo3nn/iNVA/VBpce3l5ITk5Wficnp4ODw8Pvfnp6enC54yMDL35ZVGr1Zg1axbS0tKwadMmODhU7WLLzMyHRqOt0jrm5u7ugPT0PMiUauTlKwxaRyZTIr2oqIZrRqagO/9Uf/EaqN94/onXQN0jFouq1aBbaVpIly5dcOrUKWRlZUEul+Pw4cMICQkR5nt7e8PKygpnz54FAOzevVtvflk++ugj5OfnIyEhocqBNRERERFRbVVpy7WnpyemTp2K4cOHQ6VS4fXXX4e/vz/GjBmDSZMmwc/PDytXrsS8efOQn5+P1q1bY/jw4eWWl5WVhc2bN6Nx48YYNGiQMH337t3G2SMiIiIiIjMRabXaupVfgbqdFlKgVCPpSlrlKwDo0MoTdlaV/v6hOoCPA4nXQP3G80+8BuqeGksLISIiIiIiwzC4JiIiIiIyEgbXRERERERGwuCaiIiIiMhIGFwTERERERkJg2siIiIiIiNhcE1EREREZCQMromIiIiIjITBNRERERGRkTC4JiIiIiIyEgbXRERERERGwuCaiIiIiMhIGFwTERERERkJg2siIiIiIiNhcE1EREREZCQMromIiIiIjITBNRERERGRkTC4riVu3c9FZo7C3NUgIiIioifA4LoW0Gq1OP1HGi7cyDB3VYiIiIjoCTC4rgVUag1URRo8zJZDo9WauzpEREREVE0MrmuBAoUaQHGQnZ2nNHNtiIiIiKi6GFzXAgUKlfD/h1lyM9aEiIiIiJ4Eg+taoEBe3HJtIREjLVtm5toQERERUXUxuK4FZAoVRCKgiac90rLk0DLvmoiIiKhOYnBdCxQo1LC1ksLT1RZKVRFyCgrNXSUiIiIiqgYG17VAgUIFW2sLeLrYAADSmHdNREREVCcxuK4FCuRq2FlL4WBrARsrCfOuiYiIiOooBtdmptVqIVOoYWdjAZFIBE8XWzxk3jURERFRncTg2swUhUXQaLWws5YCADxcbSBTqpEvV1WyJhERERHVNgyuzUzXx7Xt38G1p4stAOZdExEREdVFDK7NTNfHtZ2NBQDA2d4SVhbMuyYiIiKqixhcm5ns76HPdWkhIpEIHi42eJjNlmsiIiKiusag4Hrv3r3o06cPevfujc2bN5eaf+XKFURERCAsLAxz586FWq3Wmx8bG4u4uDjhc25uLsaOHYtXXnkFkZGRSE9Pf8LdqLsKFCpIxCJYWUiEaZ6uNsiTqfAoX2nGmhERERFRVVUaXKelpWH16tXYsmULdu3ahcTERNy4cUNvmZkzZ2LBggU4dOgQtFottm7dCgDIy8vDnDlzsH79er3lY2Nj0b59e/zwww8YNGgQPvzwQyPuUt1SoCjuhk8kEgnTdHnXN1JzzFUtIiIiIqqGSoPrkydPolOnTnB2doatrS3CwsJw8OBBYf7du3ehUCjQtm1bAEBERIQw/+jRo2jWrBlGjhypV+bPP/+M/v37AwD69euH48ePQ6Wqn71jFMhVsP0731rHxcEKFhIxUu4yuCYiIiKqSyoNrh8+fAh3d3fhs4eHB9LS0sqd7+7uLswfMGAAxo4dC4nkn5SHx9eRSqWwt7dHVlbWk+1JHaVruS5JLBbB3cWGLddEREREdYy0sgU0Go1eyoJWq9X7XNl8Q2i1WojFhr9b6eZmX6Xyawt3dwdos2RwsLcGABRptJAr1XBxtBGm6Tzj5YDTfzyApY0lnOytzFFdMjJ3dwdzV4HMjNdA/cbzT7wG6odKg2svLy8kJycLn9PT0+Hh4aE3v+QLiRkZGXrzy+Lh4YGMjAx4eXlBrVajoKAAzs7OBlc6MzMfGk3dGsHQ3d0B6el5kCnVyMtXAIAwUIyFGMI0HWe74lSRUxfuop2PO6hu051/qr94DdRvPP/Ea6DuEYtF1WrQrbS5uEuXLjh16hSysrIgl8tx+PBhhISECPO9vb1hZWWFs2fPAgB2796tN78s3bt3x65duwAABw4cQPv27WFhYVHhOk+jArluAJnS++7qWNySfS+zwKR1IiIiIqLqqzS49vT0xNSpUzF8+HAMGDAA/fr1g7+/P8aMGYNLly4BAFauXImYmBiEh4dDJpNh+PDhFZY5efJkXLhwAX379sWWLVuwYMEC4+xNHVOg6+PapvQDBKlEDEc7S6Szv2siIiKiOkOk1WrrVn4F6nZaSIFSjaQrxS98/nEzE+f+zMCbvZ6HhbT075xfL96HVCLG7MhAU1eXjIyPA4nXQP3G80+8BuqeGksLoZpToFDD0kJcZmANAA2cbZD+iC3XRERERHUFg2szKpCrYFdGvrWOu5M1svOUKFQVmbBWRERERFRdDK7NqEChhq11+R22NHC2AQC2XhMRERHVEQyuzUimUFfYct3AubjHkIcMromIiIjqBAbXZqIu0kCpKio1OmNJ7k5/t1yzxxAiIiKiOoHBtZkUyMvvhk/H1loKGyspW66JiIiI6ggG12ZSoCgeQKaitBCRSAQPZxsG10RERER1BINrM9ENIFPRC40A4O5iw7QQIiIiojqCwbWZyBTlD31ekoezDTJyFHVu0BwiIiKi+ojBtZkUKNSwsZJAIhZVuJyHiw2KNFpk5SpMVDMiIiIiqi4G12ZS2QAyOu5/93XNvGsiIiKi2o/BtZnIKhlARseDwTURERFRncHg2gy0Wi0KFIa1XLs4WEEqEeEhX2okIiIiqvUYXJtBoVoDdZG2wj6udcRiEdyd2WMIERERUV3A4NoMCuSV93Fdkjv7uiYiIiKqExhcm4Hs7z6uKxr6vCTdQDJaLbvjIyIiIqrNGFybQYGBfVzruLvYQFlYhDyZqiarRURERERPiMG1GRTI1RCLABsriUHLs8cQIiIiorqBwbUZFChUsLW2gEhU8QAyOh4uxcE1X2okIiIiqt0YXJuBUlUEa0vDWq0BoIGTDURgyzURERFRbcfg2gxUag0spIYfegupGC6OVuzrmoiIiKiWY3BtBiq1BpZVCK6B4rzrdLZcExEREdVqDK7NoFCtgYXU8LQQgH1dExEREdUFDK7NoKppIUDxS425BYWQK9U1VCsiIiIielIMrk1Mq9VWM7i2BQCmhhARERHVYgyuTUypKgKAqgfXf/d1zeCaiIiIqPZicG1iisLqBdfuHEiGiIiIqNZjcG1iCmX1gmtbaynsbSw4kAwRERFRLcbg2sQUhcUvJFa1Kz6APYYQERER1XYMrk2sumkhQHGPIRxIhoiIiKj2YnBtYrqW66r2cw0Ut1xn5SqhLtIYu1pEREREZAQGBdd79+5Fnz590Lt3b2zevLnU/CtXriAiIgJhYWGYO3cu1OriAPLevXuIjIxEeHg4JkyYgIKCAgBATk4OxowZg3/96194/fXXceXKFSPuUu32RC3XzjbQaLXIzFEYu1pEREREZASVRnhpaWlYvXo1tmzZgl27diExMRE3btzQW2bmzJlYsGABDh06BK1Wi61btwIAFi9ejKFDh+LgwYPw9fVFfHw8AGD9+vV44YUXsGfPHkRHR+ODDz6ogV2rnar7QiMANPawAwD8lZZn1DoRERERkXFUGuGdPHkSnTp1grOzM2xtbREWFoaDBw8K8+/evQuFQoG2bdsCACIiInDw4EGoVCokJSUhLCxMbzoAaDQaoRVbLpfD2tra6DtWWwlpIZJqBNfu9pBKxLh1P9fY1SIiIiIiI5BWtsDDhw/h7u4ufPbw8MDFixfLne/u7o60tDRkZ2fD3t4eUqlUbzoAjBo1CoMHD0ZwcDAKCgqQkJBgtB2q7RSFRZBKRBCLRVVeVyoRo6mXPW7dY3BNREREVBtVGlxrNBqIRP8EglqtVu9zefMfXw6A8HnJkiWIjIzE8OHDcf78eUydOhX79++HnZ2dQZV2c7M3aLnaxt3dAWqNFpYWEjjYV95ab2trBXdXW71prVs0wKEzf8HV1Q6SarR+k/m4uzuYuwpkZrwG6jeef+I1UD9UGlx7eXkhOTlZ+Jyeng4PDw+9+enp6cLnjIwMeHh4wNXVFXl5eSgqKoJEItFb7+jRo0KedUBAANzc3JCSkgJ/f3+DKp2ZmQ+NRmvYHtYS7u4OSE/PQ56sEFKxCHn5lb+UKJMpkV5UpDfNy9kaysIiXLjyAM948ktaV+jOP9VfvAbqN55/4jVQ94jFomo16Fba9NmlSxecOnUKWVlZkMvlOHz4MEJCQoT53t7esLKywtmzZwEAu3fvRkhICCwsLNC+fXscOHAAALBr1y5hvZYtW+LHH38EANy+fRsPHz5E8+bNq1z5ukhRWFStbvh0mjdyBADmXRMRERHVQpUG156enpg6dSqGDx+OAQMGoF+/fvD398eYMWNw6dIlAMDKlSsRExOD8PBwyGQyDB8+HACwcOFCbN26FX369EFycjKmTJkCAFi+fDl27NiBfv36Ydq0afjoo4/g4FA/WmEVSnW1egrR8XC2gZ21lME1ERERUS0k0mq1dSu/AnU7LWTe12cgEYvQI9C70nU6tPKEnVXpzJ1VWy/gUZ4SH4zuWBNVpRrAx4HEa6B+4/knXgN1T3XTQirNuSbjUhSq4WxvZdCyIrEIBUp1qemN3e1x+VYWsvKUsLL8J8XEykKKJ2gUJyIiIqInxODaxIpzrg2LgJWqIvz+Z3qp6YWqImi1wJGkO/As0ZtIh1aekJbR0k1EREREpsF2ThPSarVQFBbB8gmblxs4F3fjl85h0ImIiIhqFQbXJqRSa6DRaJ/ohUYAsLaUwt7GApmP5EaqGREREREZA4NrE5IXFvdZ/aTBNQA0cLJGBluuiYiIiGoVBtcmpPj75cQn6edap4GTNQoUasjLeOGRiIiIiMyDwbUJyQt1wbURWq7/zrtm6zURERFR7cHg2oTkSuOlhbg6WkMkAjKYd01ERERUazC4NqF/0kKe/LBLJWI421ux5ZqIiIioFmFwbUK6tJAn7YpPR/dSYx0cZJOIiIjoqcTg2oSMmRYCAA2cbaBSa5BboDJKeURERET0ZBhcm5DCiC80AsUt1wCQkcO8ayIiIqLagMG1CcmVRZBKRJCIjXPYnewtIZWImHdNREREVEswuDYheaEa1pZSo5UnFong5sjBZIiIiIhqCwbXJqRQqmFt+eQDyJTk7myD7FwFClVFRi2XiIiIiKqOwbUJyZVFRg+um3jaQ6MF7qTlG7VcIiIiIqo6BtcmpDByWghQ/FKjvY0Fbj/INWq5RERERFR1DK5NqCZarkUiEZo1dMD9TBnyZIVGLZuIiIiIqobBtQnJC9WwtjJucA0AzRs6QKsFfr+eYfSyiYiIiMhwDK5NqPiFRuOmhQCAs70VnOwscfZautHLJiIiIiLDMbg2IXmh8dNCgH9SQ1Lu5iArl93yEREREZkLg2sTUak1UKk1NdJyDQDNGzpCCyD56sMaKZ+IiIiIKsfg2kTkyuKhz2ui5RoAHO0s0cTDHmeuMLgmIiIiMhcG1yYiU6gA1FxwDQCBPu64dT8XDx/Ja2wbRERERFQ+BtcmIrRcW9VMWggABL7gDgD47b9pNbYNIiIiIiofg2sTkSlqNi0EAFwdrfGctxN+u8LgmoiIiMgcGFybyD9pITXXcg0AHV/0RGp6Ae6mczh0IiIiIlNjcG0ipmi5BoD2Pu4QiYDf+GIjERERkckxuDYRWQ33FqLjZG+FVk1dcOKP+yjSaGp0W0RERESkj8G1ich1aSE1+EKjTmi7xsjKVSL5KkdsJCIiIjIlBtcmIlOoIRIBltKaP+RtnmsAT1dbHPrtDrRabY1vj4iIiIiKMbg2EZlSDRtLKUQiUY1vSywSoXeHJrj9IA9//u9RjW+PiIiIiIoZFFzv3bsXffr0Qe/evbF58+ZS869cuYKIiAiEhYVh7ty5UKuL84vv3buHyMhIhIeHY8KECSgoKAAA5OfnY/r06RgwYAAGDBiAy5cvG3GXaieZQgUbq5rNty6pi68X7G0scOi3/5lsm0RERET1XaXBdVpaGlavXo0tW7Zg165dSExMxI0bN/SWmTlzJhYsWIBDhw5Bq9Vi69atAIDFixdj6NChOHjwIHx9fREfHw8AiImJQcOGDbFr1y5MmzYNixYtMv6e1TIyhdok+dY6VhYS9Ajwxu83MvAgS2ay7RIRERHVZ5UG1ydPnkSnTp3g7OwMW1tbhIWF4eDBg8L8u3fvQqFQoG3btgCAiIgIHDx4ECqVCklJSQgLC9ObrtVqcfjwYYwdOxYAEBISgmXLltXEvtUqckVxWkhNEolFKFCqhX+dfL0gkYhw4PRfetMLlGqo2ZEIERERkdFVGu09fPgQ7u7uwmcPDw9cvHix3Pnu7u5IS0tDdnY27O3tIZVK9aZnZmbC0tISW7ZswU8//QQrKyvMmTPHmPtUK8mUKljXcFqIUlWE3//U7yGkWUNHnPrjARo1sNUbwKZDK09Iq9CSrtYASpXaoGWtLKQwwXubRERERLVOpdGVRqPRewlPq9XqfS5v/uPLAYBIJEJRUREyMjLg4OCAxMREnDhxAu+88w6OHj1qcKXd3OwNXra2kCnU8GjoCFtbKzjYWxu0joWF1OBly1u+w4teuJGag9sPCtDhRU9huq2tFdxdbQ0u+2GWDFdvZhq0bKCPR5XKri/c3R3MXQUyM14D9RvPP/EaqB8qDa69vLyQnJwsfE5PT4eHh4fe/PT0f1pLMzIy4OHhAVdXV+Tl5aGoqAgSiURYz8XFBVKpFP369QMAdO3aFTKZDJmZmXBzczOo0pmZ+dBo6lYXczKFGmJoIZMpkZevMGgdlUpt8LLlLW8hBrwb2OHijXQ87+0AiaS4SVkmUyK9qMjw+iv/KVuuVEMkEpU7IE5Vy64P3N0dkJ6eZ+5qkBnxGqjfeP6J10DdIxaLqtWgW+nD+y5duuDUqVPIysqCXC7H4cOHERISIsz39vaGlZUVzp49CwDYvXs3QkJCYGFhgfbt2+PAgQMAgF27diEkJASWlpbo0qUL9u/fDwC4cOECbGxs4OLiUuXK1yVypUovLcOUXmzuAkVhEVLu5j5RORqtFv+9nYXvj9/ET+dSjVQ7IiIioqdHpcG1p6cnpk6diuHDh2PAgAHo168f/P39MWbMGFy6dAkAsHLlSsTExCA8PBwymQzDhw8HACxcuBBbt25Fnz59kJycjClTpgAAPvzwQxw/fhz9+vXDokWLsHr1aojFT2+SrkajhVxZBBsT9hZSkperLdydbXD2z3TkyQqrVcb9jAIcPH0HyVfTYWUhQfojBXLyq1cWERER0dNKpK2DQ/jVtbQQmUKNibHHMaTnc+japhGSrqQZtF6bF9xLvaBY3eXzZIXYd/IvONlZIrzjM+jY2gt2BgT76iINDpz+C3tP3IZUIkaHVh7wcrXFjp9T4NvCFQEvuJdap0MrT4PKrk/4OJB4DdRvPP/Ea6DuqbG0EHpycmVxLxum7Of6cQ62lujU2hMZOQr8fiPDoHWKNBqs2XkJu/7vFto83wCvdmuGFo0cYWsthZebLW7dz+Pw6kREREQlMLg2AXlhcXBtrrQQneYNHfGctxMu3cyqdFh0rVaLTQev4WJKJiJffgEj+7TSyxl/1tsR+XIVHmbLa7raRERERHUGg2sTUCiLe86wKad3DVPq0MoDjnaW2PTD1Qrzr3f/egv/d/E++nVphtB2jUvNb+LhAKlEhJv3nuwlSSIiIqKnCYNrE9C1XJszLUTHQipGtzYNUaBQYf2Bq2Wmdfx8/i72nLiNYL+GeK1b83LLecbTAbcf5KGoiMM9EhEREQEMrk1Cl3NdG1quAcDN0RqvBrfAhRsZeG/tSWz44SqSrz5EgUKF89fT8c3ha/Br4Ybh4T6lBgIqqUUjR6jUGqSmF5iw9kRERES1l/mbUusBReHfaSG1oOVap3tAIzjbW+L89QwkXU3D8d/vQSQCxCIRmnk5IHqAL6SSin97ebnZwsZKgpv3ctHUi6NOEREREdWeaO8pJvQWYilFbelbQyQSoatfQ3T1a4gijQY37+Xi8q0sPMyWY0jo89a/UHsAACAASURBVLAyoJVdLBKheUNHXP0rG4rConJHbCQiIiKqLxhcm8A/wbUEclXtGxZcIhbj+cbOeL6xc5XXbdHIEf+9nY3bD3LR8pmne5RNIiIiosowuDYBRWERbKwkEIvLz182NZFYhIK/g35DlDdmj4uDFZztLXHrHoNrIiIiIgbXJiBXqmFjZWHuauhRqoqqPPpjWUQiEVo0csS5PzOQW1AIRztLY1WRiIiIqM5hbyEmIC8sgq310/s7pnkjRwDA7Qcc1pWIiIjqNwbXJqBQqp/q4NrO2gKujlZ4kCkzd1WIiIiIzIrBtQnIC9WwrWVpIcbm4WKD9EdyFJWXnE1ERERUDzC4NgGFsgg2T3HLNQB4utiiSKNFVq7C3FUhIiIiMhsG1yYgL3y600KA4pZrAEjLYmoIERER1V8Mrk1AriyCrfXTnRZiYyWFo50l0rLl5q4KERERkdkwuK5hWq22+IXGWjT0eU3xdLHBw2w5NMy7JiIionqKwXUNU6qKoAWe+rQQoDg1RKXW4F5mgbmrQkRERGQWDK5rmFxZPNy5zVOeFgIAnq62AICUuzlmrgkRERGReTC4rmGKwuIhxutDWoi9jQXsrKVISWVwTURERPUTg+sapmu5rg9pIUBxasiNuznQapl3TURERPUPg+saJte1XNeDtBCgODUkT6bCQ/YaQkRERPUQg+saplDqguv60XLt+Xd/19f+98jMNSEiIiIyPQbXNUx4obEe5FwDgKOdJextLPAng2siIiKqhxhc17D6lhYiEonwrLcTg2siIiKqlxhc1zD532kh9aXlGgCe9XZERo4CWbkKc1eFiIiIyKQYXNewArkaVpYSWEjrz6F+ztsJAPBnKluviYiIqH6pPxGfmeTLC2FfT1JCdLzd7WFtKcGf/2N/10RERFS/MLiuYflyNext6ldwLRaL8Fxj5l0TERFR/cPguobly1Wwt6k/+dY6Pk2ccS+jAHmyQnNXhYiIiMhkGFzXsAK5Cnb1rOUaAF5o4gwAuHqHrddERERUfxgUXO/duxd9+vRB7969sXnz5lLzr1y5goiICISFhWHu3LlQq4t7yLh37x4iIyMRHh6OCRMmoKCgQG+9Bw8eICgoCKmpqUbYldopX66Cg42luathci0aOcLR1gJJV9LMXRUiIiIik6k0uP7/9u49Pqry3vf4Z+65TO6ZSSAJCAKigKBGuXQbq1WDQo6UI1urbnpqDz1qt1Rfp2xQeam4bVHLBrcvS0+99ObGs0W0SaEY7T5oFUHlJmCBIoJckjCZXCCZZJLMZZ0/BkZDQALMMLl836/XvGbWep618lusxcpvnjzreTweD0uWLOHVV1+lvLyc1157jT179nSqM2fOHB599FHefvttDMNg+fLlACxYsIA77riDyspKRo8ezdKlS6PbhMNhHnnkEQKBQIwPqecIhcO0tgdJ7YfdQixmM1denMene+qjwxGKiIiI9HWnTa7XrVvHhAkTyMzMJCUlhdLSUiorK6PlVVVVtLW1MW7cOACmT59OZWUlgUCADRs2UFpa2mn9cS+99BKTJk0iKysr1sfUY7T4I0llf3ug8bgJl+QRDIXZvNub6FBEREREzovTJte1tbW4XK7ostvtxuPxnLLc5XLh8XhobGzE6XRitVo7rQf47LPP+Oijj/jBD34QswPpiXz+SKt8f02uhw5MJzcjiY92qGuIiIiI9A+n7a8QDocxmUzRZcMwOi2fqvzEehCZGtvv97NgwQL+/d//HbP57J6nzMlxntV251ttc2SkjIL8yKQqLlcaRkMrac6kbm1vs1m7XfdM68dz3ykpDlzZKQBcW1zEG2s+x5pkIyut+z+vL3K50hIdgiSYroH+TedfdA30D6dNrvPz89m4cWN02ev14na7O5V7vV/92b+urg632012djbNzc2EQiEsFkt0u40bN1JfX8+9994LRFq+f/SjH/H8888zdOjQbgVdX+8jHDa6fZCJUlUTmUQldKxfudfbTGt7kGZf96YFDwS6X/dM68dz362t7XhDIQAuvSCL1w2oXLuX64uLuv3z+hqXKw2vtznRYUgC6Rro33T+RddA72M2m86qQfe0TceTJk1i/fr1NDQ04Pf7eeeddygpKYmWFxQU4HA42LRpEwAVFRWUlJRgs9koLi5m9erVAJSXl1NSUsLVV1/NmjVrqKiooKKiArfbzQsvvNDtxLo3iXYL6WczNH5dgctJocvJxxo1RERERPqB0ybXeXl5PPjgg8ycOZNp06YxdepULr30UmbNmsX27dsBWLRoEQsXLmTy5Mm0trYyc+ZMAB577DGWL1/OzTffzMaNG3nggQfiezQ9jK8tklz3x3Guv27CqDy+qGqi9og/0aGIiIiIxFW3xogrKyujrKys07oXX3wx+nnkyJGsWLGiy3YFBQW88sor37jvNWvWdCeEXsnXGsBiNpFktyQ6lIS66mI3K977gk92eJg66YJEhyMiIiISN5qhMY4iU5/bujzY2d/kZiQzrDCDj3Z4MIye31deRERE5GwpuY4jnz+AM6V/dwk5bsIleVTXtXDI23L6yiIiIiK9lJLrOGrxB/r1w4xfVzzSjdlk4qMdhxMdioiIiEjcKLmOI19bsN9OIHOi9BQ7o4Zk88kOD2F1DREREZE+Ssl1HPn8gX45UojJbKKlPdjldcVIF/VN7fz10+pO64PhREcsIiIiEhvdGi1EzpxhGLT4A6T1wz7X7YEQW3d7u6w3DIPcjCRef3cPgWAIuy0yisqVF+dhdehSFBERkd5PLddx4m8PEQobpKrPdZTJZGL8JXm0d4TY8nldosMRERERiTkl13FyfAIZ9bnuLCcjiRGDMtl94Aj1Td2ffl1ERESkN1ByHSctfiXXpzJueC4Ou4WP/6Zxr0VERKRvUXIdJz4l16fksFm44iIXdUfb2FN1NNHhiIiIiMSMkus4iSbX/fCBxu4YOjAdd1Yym/9eR8uxLjQiIiIivZ2S6zjxtarl+ptEHm500xEMsXLtvkSHIyIiIhITSq7jxOcPYAJSNMTcKWWlJXHx4Cw+3H6YjbtqEx2OiIiIyDlTch0nvrYAKUlWzGZTokPp0S4b4eKCAWm8vHonVXUtiQ5HRERE5JwouY6TFn9AXUK6wWI28cOpl+Cwmnn+ze3424OJDklERETkrCm5jhOfP6CHGbsp0+ng3mmj8Tb6eWnVDsIank9ERER6KSXXceJrDeDU7IzddtGgLP7xumFs+byOtz7an+hwRERERM6Kkus48bWpW8iZuqG4kKsudvPm+3v5bF99osMREREROWNKruPE5w+QquS6W0xmEy3tQVo7Qvzjd4YzICeV/1P+N/YdbqalPdjpFQwnOloRERGRU9M4cXEQCIboCITVct1N7YEQW3d7o8vjL3Gzev0Bnnt9KzdNGITdZomWXXlxHlYNbygiIiI9lFqu48Dnj4x4oQcaz05aip1rLhtIU2sH72+t0QOOIiIi0msouY6D6NTneqDxrOVnpzD+kjyq61rY/Hfv6TcQERER6QH09/U48LV2AJr6/FyNKMrkSHM7O75sJMPpYHhhRqJDEhEREflGSq7jwNd2rFuIkutzVjzSzdGWDj7+22HS1c1GREREejh1C4mD491CNFrIuTObTZSMG4gzxc6azVUc9DQnOiQRERGRU1JyHQfRPtdKrmPCYbNwQ3EhdquZpX/8jOq6lkSHJCIiInJSSq7joMUfwGG3YLPqnzdWUpNt3HBlESYT/Ntrn1J3xJ/okERERES6UPYXBz6/pj6Ph/RUOz+ePob2jhCL/vNTjvjaEx2SiIiISCdKruPA59fU5/FS4HLy4G1jOdrSwb/956c0HRuZRURERKQnUHIdB5HkWgOxxMuFAzOYfeul1B7xs/A/NquLiIiIiPQYSq7jwOcP4EyxJzqMPu3iwVn89PZxNLd08PP/2MShWl+iQxIRERFRch0PLepzHTcms4mW9iAt7UEGupz85B/HYhiwcNkmtn5RHy07/gqGEx2xiIiI9CfdSq5XrlzJzTffzI033siyZcu6lO/cuZPp06dTWlrKI488QjAYmUSlurqaO++8k8mTJ3PvvffS0hIZQu2LL77gzjvv5JZbbuG2225j586dMTykxAqHDVrbgqSqW0hctAdCbNjpib6qvD6+U1yIzWrh+Te28cZ7ezqVtweCiQ5ZRERE+pHTJtcej4clS5bw6quvUl5ezmuvvcaePXs61ZkzZw6PPvoob7/9NoZhsHz5cgAWLFjAHXfcQWVlJaNHj2bp0qUAzJ8/n1mzZlFRUcEDDzzA3Llz43BoidHSFsBAY1yfT85kG5PHF5GV5uCvW6rZvrcewzASHZaIiIj0Q6dNrtetW8eECRPIzMwkJSWF0tJSKisro+VVVVW0tbUxbtw4AKZPn05lZSWBQIANGzZQWlraaT3AjBkzuPrqqwG46KKLqKmpifmBJYomkEmMJLuVG64sYnB+Glt21/HXT6vpCIYSHZaIiIj0M6dNrmtra3G5XNFlt9uNx+M5ZbnL5cLj8dDY2IjT6cRqtXZaD5FE22KxAPDcc89x/fXXx+ZoeoBocp2i5Pp8s1nNXD12AMUjXRys9fHW+gMcbmhNdFgiIiLSj5y2Y3A4HMZkMkWXDcPotHyq8hPrAV3qPfPMM2zdupU//OEPZxR0To7zjOqfT3s9kVErigZk4nKldSpzudIwGlpJcyZ1a182m7Xbdc+0fl/e9/jRyRS603n74/0s+r9buH/GOEouK+hyPZ5vJ14P0v/oGujfdP5F10D/cNrkOj8/n40bN0aXvV4vbre7U7nX640u19XV4Xa7yc7Oprm5mVAohMVi6bRdMBhk7ty5eDwe/vCHP5CWdmYXW329j3C4Z/aprfI0ARBo68DrbY6ud7nS8HqbaW0P0uxr69a+AoHu1z3T+n193+kpVm6eOIhNu7wsWraJN9bs5rslQxl1QXZCkuzj51/6L10D/ZvOv+ga6H3MZtNZNeietlvIpEmTWL9+PQ0NDfj9ft555x1KSkqi5QUFBTgcDjZt2gRARUUFJSUl2Gw2iouLWb16NQDl5eXR7Z5++ml8Ph+/+c1vzjix7ula/JHRKVLV5zrhUpNsPHDbOH5w00iaWjpY/NpWnl62md0HjyQ6NBEREemjTttynZeXx4MPPsjMmTMJBALceuutXHrppcyaNYvZs2czZswYFi1axPz58/H5fIwaNYqZM2cC8NhjjzFv3jx+9atfMWDAABYvXkxDQwPLli2jsLCQGTNmRH9ORUVF/I7yPGr2d2Axm0iyWxIdigAWs4mrxw5kwqh83t9azap1X/LUss0ML8ygZOxAike6cdh0rkRERCQ2ujUYc1lZGWVlZZ3Wvfjii9HPI0eOZMWKFV22Kygo4JVXXumyfseOHWcaZ6/R4g/gTLElvH+vRByfdAZgwuh8Lr/IxdrtNazdWs3Lf97Jsr/spnikm0mj8xlWmIVV0yqJiIjIOdBMJzHm8wc1DF8P0h4IsXW3t9O6tGQbk8cPwtPoZ8+ho6z/7DBrt9UwoiiT731nOIPz+1ZXJRERETl/lFzHmE9Tn/cKJpOJ/OwU8rNTuOpiN3sOHWXH/kae+N0GJozKZ3rJUHIyuj/iiYiIiAgouY65Fn+A/OyURIchZ8Bus3DJkGxmXDec9zYf4i8bD7FhVy03XFnIf5s0BIf6z4uIiEg3KbmOMZ8/oJFCeqmUJCszrh3GtZcX8Mf39/LWRwfY9Hcv/3PKJQwrzEh0eCIiItIL6PGtGDIMA58/QJpmZ+zVcjOSmVU2irl3XEY4bLBw2SZef28PgWA40aGJiIhID6fkOobaOkKEwgap6nPdJ1w0KIsFd1/F1ZcO5K2PDvDE7zdwwKMJAEREROTUlFzHkM8fANBoIb3U8WH7vv4KAzOuG8Y900bT3BrgX3+/kdUf78fXFkAN2SIiInIi9bmOISXXvdvJhu37upvGD+LD7TWsePcLNu6s5cf/fQw5aRpRRERERL6ilusYOurrAFCf6z7KYbdw7eUFXD4il/2Hm/nFq1s45PUlOiwRERHpQZRcx9CXh5swmaDAlZroUCROTCYTo4fmcMNVRbS1h3jy9xv5cHtNosMSERGRHkLJdQztrW6iINdJkl29bfq6/OwU5t51OUMHpvPyn3fyu7d20hEIJTosERERSTAl1zFiGAb7apoYOjA90aHIeZKeaud/3z6OKRMH8/7WGn7+yiZqG1sTHZaIiIgkkJLrGPE0+mlpCyq57kdMZhNtgTCTJwzmf90yirqjbTz+2w18+NnhLqOONLd2JDpcEREROQ/UfyFGvqg6CqDkuh85cXSRyRMG8ddPq3l51Q6GF2Zw2YjcaBeha64YhClRgYqIiMh5o5brGNlb04TDbmFgjh5m7K+cyTYmjy/ikguy2FN1lPL397FzfyPhsJHo0EREROQ8Uct1jOytbmJIfhpms9on+zOL2UzxSDfDCjLYsKuWDTtr2X3wCHm5TkYNykx0eCIiIhJnSq5joCMQ4lCtj9KrBiU6FOkhMtMcXF9cyMFaHxt3efm3VzczMDeVscNyGDcslwsHZuiLmIiISB+k5DoGDnh8hMIGF6q/tXyNyWRiUF4aBbmpGCYzm3d5eOeTg7z10QGcyTbGDM3mggHpFLmcFLqdmtlTRESkD1ByHQN7q/Uwo5yaxWLmW+MKmTgqj9a2IDv3N/DZ3ga2721g/d880XqZTjsDclLJzUwiLyuVvKxkXJlJ5GYkk+ywYDKppVtERKSnU3IdA3trmshJd5DhdCQ6FOmh2gMhNu78KpG+5IIsLh6cSVtHiMbm9ujL09jKF1VH6QiGO23vsFvITnOQFX0lkZ3mIDPNQXaag+z0JLV8i4iI9ABKrmPgi6omhgzMSHQY0suYTCaSHVaSHVYG5nYeZWb0hTl4G/3UHWmj0dfOkeZ2jvgir+q6Fo62dGCcMAhJTnoSw4syGFGYyfCiTAbkpGBWa7eIiMh5peT6HB1t6aC+qY3vXFGY6FCkD7FYzHgaIrM9pqfYSE+xMSjPGS0Phw3aOoK0tAUZ6HJSf8TPl4eb+du+Bj461tXEmWzj4sFZjBqazSUXZJPsiPx3d9isWDUIp4iISFwouT5H6m8tiWA2m0hJspGSZOOSIdls3e3l0gtzGDM0m+bWALWNfg43tLLti3o27KrFZIK8rBQG5qYwacwALirKJDVJ3UhERERiTcn1Odpb3YTZZGJwflqiQxHBZDKRnmonPdXOsMIMwoZB3ZE2DtX6OOT1sXl3HZt31wGQm5HE4Lw0Clyp5GWnkJeVQl52spJuERGRc6Dk+hztrW6i0J2Kw2ZJdCgiXZhNJtxZybizkrn8IhdtHUFcWSkc9Pg4WOvjQG0zm3d7+Xr37dQkK4VuJxcWZDByUBbDCzN0fYuIiHSTkutzEA4b7KtpYuKo/ESHItItSXYrQwsyaG7pINOZzZih2YRCYZr9AZpaOmhujbzXNvr5+4EjrF6/H4s58peZMUMjE+AMynNqWEAREZFTUHJ9DmrqW2jrCKm/tfRqFouZTKeDzBOGkuwIhMhMS+Kgp5ld+xv509p9VKzdR066g3HDXIwdnsOwggyS7LqNiIiIHKffiudgb3UToIcZpW+y2yyMvjCHUUOymTxhMM2tHZHJb76o5/1t1fy/zYcwmaAgN5ULBqQzZEA6FxZkkJvuINlhVeu2iIj0S0quz8HemiaSHVbyslMSHYpIXLQHQmzd7Y0uWy0mLhuRy5gLs/E0+PEeibw+/puHtdtqovVsVjMZqXYynZFE22I2YTGbMB977/zZjMViIjU5MuRgeqqd9BQ7GU472WlJmM1K0kVEpPdQcn0O9lY3MXRAmibqkH7HajFT4EqlwBWZ/CZsGBz1dZCTkcxRXztNLR0cbemIvocNg1AofOzdIGwYhMMQCocJhQ2CoTAdgfBJfo4JV2Yy7qwUBuamMjAnhfzsFPKyUzQjpYiI9EhKrs/SnqqjVHlbGDtxcKJDEUk4s8lEVpqDMcNy2LrbS1qKLZp4n8rYEa5OreKhUJi2jtCxV5DWtiBNrR0cbQmwr6aJ7XvrCYe/GtfEmWwjPyeF3PSk6PCDaSk2MlLtpKXYo+82zZgjIiLnkZLrs/DJTg8vrdpJbmYS3x43MNHhiPQJFouZ1GQzqadokb5ipJu6I23UNrbiafRT29BKbaOfPVVHaWrtOGnLN0Cyw0p6qp0sp52stCSy0x1kpUVe2WlJZKU5cKbY9BcoERGJiW4l1ytXruRXv/oVwWCQ73//+9x5552dynfu3MkjjzxCS0sLxcXFLFiwAKvVSnV1NXPmzKG+vp4hQ4awaNEiUlNTaWpq4qc//SkHDx4kOzubZ599FpfLFZcDjCXDMFi17kv++ME+RhRm8OPpY0hLsSc6LJF+IRg22H848hBxZEr4DIYVZkTLA8EwbR1B2jpCFOWl0dTSga81QHPrV91Tdh1ojHRTCRud9m21mMh0OshItZPssJLksJLisJBkt+KwWbDbzNitFmw2Mw6rBZvVjN1mwW41R9fZ7RYcNgsOW6RMybqISP902uTa4/GwZMkS3nzzTex2O7fffjvjx49n2LBh0Tpz5szhySefZNy4cTz88MMsX76cO+64gwULFnDHHXcwZcoUfvnLX7J06VLmzJnDs88+S3FxMS+88ALl5eX87Gc/49lnn43rgZ6rQDDM7yt3se6zw0wclcf/uOli/blZpAexWc3YrHbSUuCiwVls3e0lO91BdnrnIQbDhkFbe4jW9gCtbUFa2oJkOB0caW6jqSVAsz+A96gff3uItvYgHcGTt4ifjtVixmyKzJqJCSxmE4bBV+uIfOYkSXiXNSawms1YrWasFhNWixmb1YzjWJIf+QJgiX4RcHzts9lkwiDSOGAYkffwsffI+s5lx+sCkYdOTccePrWYIw+imo49iGo54fNp6losZmyWyMOrNosZ67HP+hIiIn3NaZPrdevWMWHCBDIzMwEoLS2lsrKSf/7nfwagqqqKtrY2xo0bB8D06dN57rnnmDFjBhs2bOCXv/xldP1dd93FnDlzeO+991i2bBkAU6dO5YknniAQCGCzde8BpUSMHlC+di97qo5y540juP6KwrMaZsxsjvxSTOnm9NJnUvdM62vf53vfpl4ad9/ctzMZIDm6fPGQbHbuazhp3UgyGnkQMxQOM3xQFh2BEMFgmEDIIBAIRR/I7AiFCQRDBAJhAqEwGIDJRDgcxmKxEAgEAaLJ7YlMJhOG0TWZDxsQCkf6pYfCkYdDA6EwoZBBeyBEq6+dQNAgEAzREQxxkl33WJZjybjVbMJkNkW+WJiOv0e+aBy/35r42hePYx8i311M0brHtzUfS+6tFhNmsxmrOfKzzMcTf3Pki8qJd/Kv39pP9iXnG0pPWnJ8fw6Hnfb2jk6lJ/4a6bx70zeUfcPP+6bCkwR6vP7JYonuq/Pb15ZNXb8bnuTfr8u+v3Y+u+ybr8q67rvrdj1F1yupq5SUelpbO85DNKdm8A03h1MUfePt5CxuNt+0yamK7DYLV450k2Q/v7MFn22+edrkura2tlOXDbfbzbZt205Z7nK58Hg8NDY24nQ6sVqtndafuI3VasXpdNLQ0EBeXl63gs7K+uYHpeLhvhmXnfM+cnKcABQOyDhNza8MLcw6o59xJvW17/O776K8MxsPvafErX2LiIh032n7NYTD4U6ttIZhdFo+VfmJ9YBTtvYahoHZrC4WIiIiItK7nTajzc/Px+v9argsr9eL2+0+ZXldXR1ut5vs7Gyam5sJhUJdtnO73dTV1QEQDAZpaWmJdjsREREREemtTptcT5o0ifXr19PQ0IDf7+edd96hpKQkWl5QUIDD4WDTpk0AVFRUUFJSgs1mo7i4mNWrVwNQXl4e3e6aa66hvLwcgNWrV1NcXNzt/tYiIiIiIj2VyTjZUzUnWLlyJb/+9a8JBALceuutzJo1i1mzZjF79mzGjBnDrl27mD9/Pj6fj1GjRrFw4ULsdjtVVVXMmzeP+vp6BgwYwOLFi8nIyODIkSPMmzePgwcPkpaWxqJFiygsLDwfxysiIiIiEjfdSq5FREREROT09BShiIiIiEiMKLkWEREREYkRJdciIiIiIjGi5FpEREREJEaUXMfZypUrufnmm7nxxhujU75L3/P8888zZcoUpkyZwjPPPAPAunXrKCsr48Ybb2TJkiXRujt37mT69OmUlpbyyCOPEAwGExW2xMHTTz/NvHnzgFOf6+rqau68804mT57MvffeS0tLSyJDlhhZs2YN06dP56abbuLJJ58EdB/oTyoqKqK/B55++mlA94B+y5C4OXz4sHHttdcajY2NRktLi1FWVmZ8/vnniQ5LYuzDDz80brvtNqO9vd3o6OgwZs6caaxcudK45pprjAMHDhiBQMC4++67jffee88wDMOYMmWKsWXLFsMwDOOhhx4yli1blsjwJYbWrVtnjB8/3pg7d65hGKc+1z/60Y+MVatWGYZhGM8//7zxzDPPJCZgiZkDBw4Y//AP/2DU1NQYHR0dxve+9z3jvffe032gn2htbTWuvPJKo76+3ggEAsatt95qfPjhh7oH9FNquY6jdevWMWHCBDIzM0lJSaG0tJTKyspEhyUx5nK5mDdvHna7HZvNxoUXXsiXX37J4MGDKSoqwmq1UlZWRmVlJVVVVbS1tTFu3DgApk+frmuijzhy5AhLlizhnnvuATjluQ4EAmzYsIHS0tJO66V3+8tf/sLNN99Mfn4+NpuNJUuWkJycrPtAPxEKhQiHw/j9foLBIMFgEKvVqntAP2VNdAB9WW1tLS6XK7rsdrvZtm1bAiOSeBg+fHj085dffslbb73FXXfd1eXcezyeLteEy+XC4/Gc13glPh599FEefPBBampqgK7//4+f68bGRpxOJ1artdN66d32KP+QSwAABd9JREFU79+PzWbjnnvuoaamhm9/+9sMHz5c94F+wul08pOf/ISbbrqJ5ORkrrzySmw2m+4B/ZRaruMoHA5jMpmiy4ZhdFqWvuXzzz/n7rvv5l/+5V8oKio66bnXNdE3vf766wwYMICJEydG153qXJ/snOsa6P1CoRDr16/n5z//Oa+99hrbtm3j4MGDug/0E7t27eKNN97g3Xff5YMPPsBsNvPhhx/qHtBPqeU6jvLz89m4cWN02ev14na7ExiRxMumTZuYPXs2Dz/8MFOmTOGTTz7B6/VGy4+f+/z8/E7r6+rqdE30AatXr8br9XLLLbdw9OhRWltbMZlMJz3X2dnZNDc3EwqFsFgsui/0Ebm5uUycOJHs7GwArr/+eiorK7FYLNE6ug/0XWvXrmXixInk5OQAka4eL7/8su4B/ZRaruNo0qRJrF+/noaGBvx+P++88w4lJSWJDktirKamhh//+McsWrSIKVOmADB27Fj27dvH/v37CYVCrFq1ipKSEgoKCnA4HGzatAmIPF2ua6L3++1vf8uqVauoqKhg9uzZXHfddSxcuPCk59pms1FcXMzq1asBKC8v1zXQB1x77bWsXbuWpqYmQqEQH3zwAZMnT9Z9oJ8YOXIk69ato7W1FcMwWLNmDVdddZXuAf2UyTAMI9FB9GUrV67k17/+NYFAgFtvvZVZs2YlOiSJsSeffJI33niDQYMGRdfdfvvtXHDBBSxcuJD29nauueYaHnroIUwmE7t27WL+/Pn4fD5GjRrFwoULsdvtCTwCiaU333yTTz75hKeeeuqU57qqqop58+ZRX1/PgAEDWLx4MRkZGYkOXc7RihUr+N3vfkcgEOBb3/oW8+fP5+OPP9Z9oJ944YUXePPNN7HZbIwZM4bHHnuMffv26R7QDym5FhERERGJEXULERERERGJESXXIiIiIiIxouRaRERERCRGlFyLiIiIiMSIkmsRERERkRjRJDIiIr3AoUOHuOGGGxgxYkR0nWEYzJw5k6KiImbOnMm0adN4+umnO233T//0T3z22Wds2bIFgN/85je88cYbWCwWsrOzeeKJJzoNI3ncU089RWVlZXR4sCFDhvDss8/G8QhFRPoGJdciIr1EUlISFRUV0WWPx8PUqVO5//77cblcvPvuu/j9fpKTkwGoqqpi37590frr1q1jxYoVLF++HKfTybJly3jooYdYtmxZl5+1ZcsWFi9ezOWXXx7/AxMR6UPULUREpJfKy8tj8ODBuN1uMjMzueKKK/iv//qvaHl5eTllZWXR5dzcXB5//HGcTicAY8aMobq6ust+Ozo62LFjBy+99BJlZWXcf//9J60nIiJdKbkWEemltmzZwoEDB2hrawNg2rRpnVq233rrLaZOnRpdHjFiBFdddRUQSaAXLVrE5MmTu+zX4/EwYcIEHnjgAf70pz8xduxY7rvvPjTnmIjI6albiIhIL9HW1sYtt9wCQCgUIisri1/84hckJSUBcO211/L4449TV1fH/v37GTp06EmnVG5oaGD27Nk4nU4efPDBLuVFRUW8+OKL0eUf/vCHLF26lEOHDlFUVBSnoxMR6RuUXIuI9BIn9rk+7uOPPwbAbrdz44038uc//5k9e/bw3e9+t0vdXbt2cd9993H99dczd+5cLBbLSevs2rWLadOmRdcZhoHNZovh0YiI9E3qFiIi0odMmzaNP/7xj2zYsIGrr766U9nhw4f5/ve/z3333cfDDz980sQawGw287Of/YyDBw8C8Oqrr3LRRReRn58f9/hFRHo7tVyLiPQhl112GX6/n+uuuw6rtfMtfunSpfj9fl555RVeeeUVINLa/frrr7N9+3bmz59PRUUFI0aMYP78+dx7772EQiHy8/NZvHhxIg5HRKTXMRl6QkVEREREJCbULUREREREJEaUXIuIiIiIxIiSaxERERGRGFFyLSIiIiISI0quRURERERiRMm1iIiIiEiMKLkWEREREYkRJdciIiIiIjHy/wHoCUq5QqHKmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.distplot(data['PM2.5'],bins=50)\n",
    "plt.title('Distribution of the hourly recorded PM2.5 concetration in the air\\nin Aotizhongxin-Beijin',\n",
    "          fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5><b>what pattern does the amount of PM2.5 concentration in the air recorded in an hour follow\n",
    "        for a daily time period ?</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find the daily average of PM2.5 contained in the air in any given hour\n",
    "daily_data = data[['date','PM2.5']]\n",
    "daily_data = daily_data.set_index('date')\n",
    "daily_data = daily_data.resample('D').median()\n",
    "decomposition = seasonal_decompose(daily_data,model='addictive')\n",
    "\n",
    "# plot the data\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    decomposition.trend.plot(figsize=(12,5),style='k-',linewidth=.9,legend=False)\n",
    "    plt.xlabel('Date',fontsize=14)\n",
    "    plt.ylabel('PM2.5 concentration (ug/m^3)',fontsize=14)\n",
    "    plt.title('Daily trend in the hourly recorded PM2.5 concentration in\\nthe air in Aotizhongxin-Beijin',fontsize=16)\n",
    "    plt.grid(axis='x')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5><b>In which month does the amount of PM2.5 contained in the air rises ?</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_data = data[['month','PM2.5']]\n",
    "months = ['January','February','March','April','May','June','July',\n",
    "         'August','September','October','November','December']\n",
    "ordered_monthdf = pd.DataFrame(months,columns=['month'])\n",
    "map_dict = {}\n",
    "for i,j in enumerate(months):\n",
    "    map_dict.setdefault(i+1,j)\n",
    "\n",
    "monthly_data.month = monthly_data.month.map(map_dict)\n",
    "monthly_average = monthly_data.groupby('month').median()\n",
    "monthly_average = pd.merge(ordered_monthdf,monthly_average,left_on='month',right_index=True)\n",
    "monthly_average = np.round(monthly_average,1)\n",
    "monthly_average = monthly_average.set_index('month')\n",
    "\n",
    "# plot the data\n",
    "with plt.style.context('ggplot'):\n",
    "    monthly_average.plot(figsize=(12,5),legend=False,kind='bar',linewidth=.9)\n",
    "    plt.xlabel('Month',fontsize=14)\n",
    "    plt.ylabel('PM2.5 concentration (ug/m^3)',fontsize=14)\n",
    "    plt.title('Monthly average of the hourly recorded PM2.5 concentration in\\nthe air in Aotizhongxin-Beijin',fontsize=16)\n",
    "    plt.grid(axis='x')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5><b>At what time of the day do we expect the amount of PM2.5 concentration in the air to be high ?</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hourly_data = data[['hour','PM2.5']]\n",
    "hrs = ['12 AM','1 AM','2 AM','3 AM','4 AM','5 AM','6 AM','7 AM','8 AM','9 AM','10 AM',\n",
    "      '11 AM','12 PM','1 PM','2 PM','3 PM','4 PM','5 PM','6 PM','7 PM',\n",
    "      '8 PM','9 PM','10 PM','11 PM']\n",
    "hour_dict = {}\n",
    "for i,j in enumerate(hrs):\n",
    "    hour_dict.setdefault(i,j)\n",
    "    \n",
    "hourly_data = hourly_data.groupby('hour').median().reset_index()\n",
    "hourly_data.hour = hourly_data.hour.map(hour_dict)\n",
    "hourly_data = hourly_data.set_index('hour')\n",
    "\n",
    "# plot the data\n",
    "with plt.style.context('ggplot'):\n",
    "    hourly_data.plot(figsize=(12,8),legend=False,kind='barh',linewidth=.9)\n",
    "    plt.ylabel('Hours',fontsize=14)\n",
    "    plt.xlabel('PM2.5 concentration (ug/m^3)',fontsize=14)\n",
    "    plt.title('Average recorded PM2.5 concentration in the air in Aotizhongxin-Beijin\\nby the hour of the day',fontsize=16)\n",
    "    plt.grid(axis='y')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5><b>In which direction does polluted air/wind mostly move ?</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cFN4Hd+7cEczNzYVff/1VEISC92DLli2FR48eicvExMQIUqlUSE1NFQSh4L1ia2srzj927Fipx8zYsWOFX375RWHarFmzBGdnZ/F18X1S2M6nT5+qdbwOGTJEYRlfX19h2LBhgjJF6y18/0ybNk2cn5+fLzg7OwszZ85UWoay7RAZGSlOe/bsmSCVSoWYmBhBEFQ7txb36tUrYeDAgcKhQ4cUpgcEBAiff/65+LroPijteK6s7SAIgvDzzz8r7JMHDx4IMplM2Lhxo7jM/v37BalUKhw+fFhh3a+++kppOwRB8bxalFwuFwICAgQPDw+xXlX2gTrnw7Nnz4rLFJ4Pz58/LwiCIPTq1Uv8TCg0c+bMEp9vgYGBgqurq7Br1y5BKpUKv//+uzivorZ/3759BalUWuLPyspK4byjyvm66DEoCILg6ekpnu9Lm9elSxfxfCsIgjBy5MjXfs4RFeIVLKpSnj9/jv379+PTTz9FRkYGMjIy0Lp1a9SoUUO8itWmTRsYGBiIV7GSk5Nx48YN8dfHxMRENG7cGI0bNxZ/EdPR0YGDgwOOHTumUF+zZs3E/01NTbFhwwZIpVLcunULf/75J1auXImnT5+Kv5AfP34cenp6cHFxEderV68e7OzsxNeFXbzatm0r1p+Xl4d27drh1KlTCr+2FyeTyRQGGzAzMxO70Zw9exa5ubklrmZ06dIFubm5OHv2rIpbuWTbi6tWrRq6dOmCHTt2ACjotrlz587XXr0CSvZxT0xMRI0aNeDk5CRuB6BgHxbui9OnT6N58+b46KOPxPXMzc1x4MABtGjRAklJSXBxcVEo18jICK6uriW60xVt06lTp6Cvr6/Qr9/CwgJmZmZqxXfq1Ck4OTkp7JfiXfOUOXToECwsLGBhYQFLS0t4enpi/fr1GDBggML9V4V8fHzw5MkT8Urn/v37Ub9+/RJXqorbtm0bpk2bBh8fn1LLLTRgwADExMRgypQpcHJyQo8ePbBs2TJcv34dO3fuLHWd06dPQ09PT+F+FGNjYxw4cADu7u5ISkpC8+bNFe410dbWxieffFJi/xTdF/Xr1wcAvHz5UqzHyckJurq64jIdOnTAnj17YGhoiMTERNja2qJ27drivjI1NUWzZs0Uut82aNAAJiYmSutRpvi9MqGhoejZsycePXqEhIQEbNy4sczjtyh1jlcbGxuFZerVq6f24AFFt62GhgZMTEzKNQBB0XOZgYEBatWqJZ6D1Dm3FqpevTrWrl2Ltm3b4t69ezhy5AjWrVuH69evIzc397WxvO4cpUx5tkN8fDxcXFygpaWFjIwM1KpVC3Z2dti8ebO4TFJSEmrVqgUPDw+FdTt37ox//vlH7W5ly5Ytw19//YWwsLAS9wW9bh+oej7U0tJS6JlQ9DjIzs7G2bNn0aFDB4V6i79XgYLeGPn5+QgKCoKPj0+Zowa+bvvn5+crfCYW3lMKAA4ODti8eTM2b96MTZs2Yfny5bCxscHgwYNx5swZAKqdr9VlZWUFiUQivq5fv36FDdxBHzYOckFVyu7du/Hy5UuEhYUhLCxMYV58fDzGjRsHbW1t+Pj4YPfu3fD398euXbvw0UcfiSMNpqWl4caNG6V22WnSpIn4f82aNUt0pfrll18QGhqKlJQU1K1bFzY2NqhevbrYB/3Zs2el3iRbp04dsZtVYZentm3bltrGZ8+eoV69eqXOKz6Sm0QiUej/XlhXUcbGxgCg9gd84XrK+Pr6YsOGDTh9+jSeP3+O1NRUMYlVtcy0tDS8fPmyRBdEoCCJAwra9bpYMjIyYG5uXmpd165dE18X358ZGRkwNDQssV7dunXVii8jIwMtW7ZUWsbrODg4iN0JJRIJatasiY8++kgsu7hGjRrB3Nwce/fuhYODA/bs2VPql56ioqOjMX/+fHh5eWHRokUKXxaKa9asWYkvrTY2Nqhdu7ZCd76iVNk/xd+TQMH7tPjQ7zo6OuL/hSOrFX7JSk9PL7Gdi0pLS8PZs2dLPa6L7o/ix1DxepQpflyfOnUK06dPx+XLl6Gnpwdzc3NUr179tWUUpc7xWlrMgprPUiq6bctbRlnlqHpuLW7//v2YN28e7t69C0NDQ1haWkJHR+e1+6S08/Obxl+au3fvij9oODk5lZh/8eJFtGrV6rXvc6CgC2zRHwdeZ9++fYiIiMD333+vkEyp0gZVz4fa2toKoxcWPQ7S09Mhl8tLvOdLO86NjIzQpk0bbN++He3atSuzba+LfcCAAQpJ4Oeff4758+cDKOiSaGVlpbBu4T2RK1asQGRkpErna3W97jOX6HWYYFGV8ttvv8Ha2hrffvutwvRr165h5syZ2L9/Pzp16oTPPvsMcXFxuHfvHnbv3q0w5LSenh5atmyJ2bNnlyhfW1tbad3Hjx/H1KlTMWLECPTt21f88OnZs6e4jImJSanP0Cg6TU9PDxKJBP/73/8URoMrVNqXflUYGBgAKLgXpWiClpKSojAfKPllsjy/yFlaWqJFixbYs2cPsrKy0Lp1a6WJoTJ6enowNjYuMShJ8WVK6z9fePVHX19fbGNRKSkpCm0uzsDAAE+fPi0xveigCarEV1o5z549U7p8UaV9aSiLt7c3Nm/ejFGjRuHIkSMYNWqU0mUXL16MyMhI+Pr6Ys6cOaW+34rasWMHTExMFL5ICoKAnJwcpe9LPT29Ut/zx44dQ8OGDaGvr48bN26UmP/kyZPX7p/idHV1S9STk5ODhIQE2NnZQVdXF23btlV47lShWrVqqVyPKjIzMxEQEAB7e3ssW7YMjRs3BlAweEHhvTBlUed4rSrKc269desWxo4di88//xyxsbHilZSxY8eKg4+8S/Hx8dDR0cHKlSsVEpL8/HwEBATgl19+wbRp05Sehwp/WFN1f16/fh0TJkxA586d4efnp3a85T0fFmVsbIxq1aqVON5KO85PnDiBHTt2QCaTYdGiRfDy8ir3Z9iMGTMUfnQpq5waNWqgUaNGuH37NgDVztdEbwu7CFKV8eDBAyQlJaF79+5wcXFR+Ovduzfq1q0rdtlwcnJC/fr1ERUVhVu3bilcWbG3t8e9e/dgZmYGKysrWFlZwdLSEtHR0fjzzz+V1n/mzBlIJBIMHz5cTK4ePXqEK1euiL9oOTo6IjMzE0lJSeJ6qampYhcGoOCqhSAIyMrKEuu3srJCQkICoqOjy/wSrIyVlRWqVatW6gAfWlpa4hU8XV1dhZEHX758iUuXLpWrzm7dumH//v34888/y+weWBoHBwekpqaiZs2aCtvi999/x7Zt2wAUdIe5evUq7t+/L653/fp1+Pv7Izk5GQ4ODkhMTFT48E9NTUVCQoJ483NpXFxckJmZqdB97ObNmwrJnCrxubi4IDExUWHEs8OHD6u9LVTl4+OD+/fvY9WqVahXr57S7oExMTGIjIyEn58f5s+fr9L76n//+x/mzJmjkIAfOnQIr169UjoktZ2dHTIyMnDy5ElxWnp6OoYOHYqjR4/CwcEB165dU/iynJOTg3379r12/xRnb2+PpKQkhS9gCQkJ8Pf3x9OnT+Hg4IAbN25AJpOJ+0kqlWL58uUKsZVFlWcS3bhxA+np6ejfv7+YXMnlcvz9998Kv26/rixVj9d3pTzPZirPufXixYvIzc2Fv7+/mFy9ePECJ0+efC+uFGzbtg3t2rWDq6urwmeOm5sb2rdvj+3bt+PVq1dwcHBAVlaWOKBFoV27dsHCwkK8uvm67ZqZmYkRI0agfv36rx0o4nXKez4sSlNTE7a2tjhw4IDC9OKjemZnZ2PKlClwdXXF+vXrIQgC5syZU664AeDjjz9WOM8WH+inuKysLFy/fl08BlU5XxO9LbyCRVVGfHw8JBJJqfe3aGpqolOnToiNjcX9+/dhZmaGzp07IyYmBjKZTOH+iZ49e2LDhg0YNGgQ/P39YWBggLi4OPzxxx/o1q2b0vqtrKwgl8vFEZz+/fdfrFixAjk5OeL9G61bt4ajoyPGjx+P8ePHo1atWlixYgWys7PFrlnm5ubw8fFBcHAwRo0ahWbNmuH48eNYsWIFhgwZUu6HThoZGaFfv35Ys2YNNDU14eTkhKSkJKxZswYDBw6Evr4+gIKuiVu3boWFhQWMjIwQFRVVrvqAgtG0lixZgurVq+PTTz9Ve31PT09YWVnB398fo0aNgqmpKf744w9s3LgRM2bMAAB88cUXiI6ORkBAAEaNGgVNTU2EhYXB2toarVu3RrNmzfDrr79i0KBBGDFiBARBwIoVK6CtrY3+/fsrrdvd3R1OTk4IDg7Gt99+i5o1ayI0NFShK4kq8fXv3x9xcXEYOnQoAgIC8PDhw1JHCKsozZo1Q/PmzbF27VoMHDiw1GUeP36MRYsWQSqVokuXLiXuv7O0tISWlhbu3LmD1NRU8b6IYcOGYejQoQgODkaPHj1w69YthIWFwcfHR+mXM09PT7Rq1Qrjxo1DUFAQDA0NsXr1apiYmKBz586QSCSIiYnB0KFDERgYCD09PURHRyMlJUWt0d/69++PX3/9FcOGDcOgQYPw4sULLFq0CN7e3mjatCkGDhyI3377DUOGDIGfnx+qVauGtWvX4syZMwgMDFS5ntq1a+Ply5fYt2+f0iTn448/Rq1atRAREQG5XI5Xr17hp59+QnJystiFSCKRoHbt2nj48CGOHj1aotuSqsfru6LKdiiuPOdWc3NzaGpqYuHChfj666/x7NkzrF27FikpKa/tUfA2nDhxAnfu3BEfKVFct27dsGfPHuzevRtdu3aFjY0NgoODERQUBFNTU2zduhVnz57FypUrxXVq166NkydPwtHRscS9dd9++y3u37+PJUuW4Pr16wo/dKj6TKkBAwaU63xY3MiRIzFw4EBMmTIFHTt2xJkzZxAbGwvg/5PE5cuX4/79+1ixYgUMDAzw7bffYvLkyejatatK3QXVkZGRofBDZXp6OqKiovDq1SsMGDAAgGrna6K3hQkWVRnbtm2Dvb29wg3qRXXt2hXr16/Hli1bMGbMGHTt2hVr164tcdOtrq4uNm7ciJCQEEyfPh05OTlo0aIFIiIiXvuh4OrqikmTJiEmJgZbtmxB/fr10alTJ2hpaSEmJgY5OTnQ1tbG0qVLMWvWLEyfPh3a2tro3bs3dHR0FO4XWLRoEcLCwrBq1So8ffoUZmZmGD9+PAYPHvxG2yg4OBiGhoaIi4tDVFQUzMzMMGHCBIUP1kmTJiE7OxvTpk2Drq4u+vTpg1atWuGff/5Ru7569eqJCWx5umFpampizZo1WLRoERYuXIjnz5+jcePGmDdvHnr06AGg4AtJbGws5s+fj4kTJ0JbWxtt27bFxIkToaWlBVNTU2zcuBELFy7Ed999B01NTTg7O2PJkiXiL+KlkUgkWLFiBebOnSt2nxs0aBD27t2rVnzGxsaIjY3FnDlzEBgYiPr162PGjBkYOXKk2ttDVd7e3uKw3qU5cuQIcnJycOXKFfTq1avE/ISEBHHY719//VW8v6rwuTTh4eEYOXIkdHV18cUXX2Ds2LFKY6lWrRrWrFmDkJAQzJ07F3K5HI6OjoiOjoaenh4AYOPGjViwYAFmzpyJ/Px82NraYuPGjWUOzlHURx99hNjYWISEhCAoKAh6enro2LGj+OW3QYMG+Omnn7Bw4UIEBwdDIpHAwsIC69atK/WeFGW6dOmC+Ph4BAYGYuzYsaUmF3p6eli2bBlCQkIwfPhwGBoawtHREWFhYRgzZgzOnj0LW1tb9OrVCwcPHsSwYcMQEhJSohxVjtd3RZXtUFx5zq1NmzbFggULsHz5cvj7+6Nu3bpo27YtvvjiC8ycOROPHj1Su+txRdm2bRt0dHSUxt62bVvo6+tj8+bN8PX1RVRUFBYtWoQlS5bg5cuXMDc3x6pVqxTutx01ahRCQ0Nx4sQJ/P333wrlFV7lK63b77x58xQG4FGmvOfD4lxdXRESEoLw8HDEx8ejVatWGD9+PObNm4eaNWvi4sWLWLt2Lfz9/dG0aVMABT+GbdmyBdOmTSvxaJI3derUKYVzWa1atWBpaYk1a9bA1dUVgGrna6K3RSK8D9fgiT4Qd+/exfnz5+Ht7S12ycrPz4eXlxc6duw9MGpGAAAgAElEQVT4xs9Het88fvwY7du3R1RUFNzc3N51OEREVAH27duHRo0aQSqVitPi4uIwffp0JCYmlvqQ36rI3d0dvr6+CA4Ofteh0AeGV7CIKtiECRPw999/i8Mtb968Gampqfjqq6/edWgV5s6dO9i2bRv27duHZs2aib8gEhFR1Xfw4EEcOXIE48ePh6mpKa5fv44lS5agW7duH0RydfXqVRw/fhwpKSkKjwAhqii8gkVUwQ4fPoyIiAhcuXIFQMG9W0FBQQrP/6jqbt++jZ49e8LY2BihoaGvHT6biIiqlqysLPz444/Yv38/nj59ChMTE3Tt2hUjR4585/fGVYSwsDBER0fDyckJixcvVnkIfSJVMcEiIiIiIiKqIBymnYiIiIiIqIIwwSIiIvqPYicWIqKKxwSLiIgqlZeXF2bOnKl0fr9+/TBs2LC3GFFJZcWoisTERMhkMpw/f77cdZe3DHVlZGRg/PjxCo9nkMlkWLNmTaXWS0T0X8BRBImI6D9v+fLl78XoaBYWFoiLi0OzZs0qtZ5Lly5h+/bt4kNagYJhuBs0aFCp9RIR/RcwwSIiov88dR56XJl0dXXf2YijH9JIp0RE7xK7CBIRvQeys7Mxe/ZsuLq6wt7eHpMnT8bixYvh5eUlLiOTybBy5Up06dIFLi4u2L17NwAgKSkJ33zzDezt7eHm5oaZM2ciKytLXK+0LnjR0dGQyWTiay8vL4SHh2PSpEmws7NDmzZtsHTpUsjlcoX11q9fD29vb1haWqJLly7YuXOnwvwnT55gzJgxcHBwgIeHB+Lj41Vqv1wux4IFC+Dk5AQXFxfMmDEDL168AABs2LABrVq1QkpKisI6U6dORY8ePUqUdenSJchkMiQmJpZo77Fjx8Rpa9asgZubGwRBUOimt3XrVri4uCAhIQHdu3eHpaUlOnfujP379yvUc+jQIfj6+sLa2hq9e/fGvXv3ymxnWduneBfBfv36YerUqRg8eDDs7e2xYMECAMDTp08xYcIEODs7w87ODgEBAbh7965CWcnJyRgyZIj4vpg0aRLS0tKQmJgIPz8/AEDPnj0xceJEACW7CBau7+zsDGdnZwQHByvsg4kTJ2LMmDGIiYmBp6cnrK2t0a9fP1y/fr3M7UBE9CFjgkVE9B74/vvvsXXrVowaNQo//vgj7ty5g3Xr1pVYbvny5fjmm28wffp0ODo64tChQ/Dz80PdunWxZMkSjB49Gjt27MCwYcNKJEdlWbduHR4/fozQ0FD06dMHkZGRCAsLU6h7wYIF6Ny5M1auXAk3NzeMGzcOu3btAgDk5+dj8ODBuHDhAmbNmoWJEydi6dKlePToUZl1Hz16FCdOnMD8+fMxZswYxMfHY9q0aQCALl26QENDQ6wHAHJycrBnzx507969RFnm5uaoW7euQjJ1/PhxAMCJEycU6vTw8IBEIilRRlZWFr7//nt88803iIyMhKGhIYKCgpCWlgYAOHPmDIYPH46mTZti+fLlcHV1xfTp01/bxvJun61bt6Jhw4ZYunQpOnXqhFevXsHPzw8nT57ElClTEBISgpSUFPTt2xfp6ekAgPv376NPnz54/vw5QkJCMGXKFBw9ehTjx4+HhYUFfvjhBwDAvHnzMGLEiBJ1Xrp0Cb169UJubi7mz5+P77//HidOnEDfvn3FxBcA/v77b8THx2Py5MlYuHAhbt++LSZsRET/VewiSET0jt28eRPbt2/HvHnzxCsyrVu3RocOHUos6+7ujj59+oivw8LCYG1tjdDQUHFaw4YNMWTIEPz5558KV8DKoqurixUrVkBbWxvt2rVDZmYmYmJiMHz4cOTk5GDVqlUYMmQIAgMDAQBt2rQRH0jaqVMn/Pnnn7h8+TLi4uLE7mZNmjQp9SpTcTVq1EBUVBT09fUBABKJBLNmzcLYsWPRsGFDtG3bFtu3b0e/fv0AFDzQOysrC126dCm1PA8PD/EKliAIOHnyJFq1aoWTJ08CKLhiWJjQlSY3NxfBwcHo3LkzAMDY2Bjdu3dHYmIifHx8EBUVhSZNmmDx4sWQSCRo27YtMjMzsWHDBqVtLO/2qVWrFqZMmYJq1aoBAH7++WfcvHkTv//+u3ivlqurKzw9PbFhwwaMGjUKMTEx0NTURFRUlPgQ1erVqyMkJAS5ublo3rw5AKBFixZo1KhRiTojIiJgZGSE1atXiw+WtbS0RNeuXbFlyxZxP2RlZSEyMhImJiYAgEePHmHOnDl49uwZDA0NX9suIqIPFa9gERG9Y0lJSQCATz75RJxWo0YNtGvXrsSyRQc/yMrKwsWLF9GxY0eFZTw8PKCvry+Wq6pPP/1U/DINAB06dMDLly9x4cIFnDlzBtnZ2Wjfvj3y8vLEv7Zt2+Lu3bu4e/cuTp06BX19fYV7eSwsLGBmZlZm3S4uLmJyBRR0WZTL5eIod76+vjhz5ozYDW7btm1wd3dHnTp1Si3Pw8MD586dw4sXL5CcnIysrCz4+fnhzJkzyM/PR1JSEvLy8uDu7q40pqLtqF+/PgDg5cuXAIBTp06VuPrl7e392jaWd/s0atRITK6Agm6EjRs3RuPGjcX9oKOjAwcHB/Gq3enTp+Hk5CQmV0DB/tyzZ49KiU9SUhI6dOig8H5o3rw5ZDKZwvuqQYMGYnIFlNxORET/RbyCRUT0jj179gzVqlUrMYpdacmDsbGx+H9mZiYEQVCYVsjIyAjPnz9XK466deuWKAMA0tPTxXu6evfuXeq6T548QUZGRqlf3ouXW5ribS2s+/HjxwCA9u3bw8DAADt37sQ333yDP//8E/PmzVNanru7O+RyOU6dOoVr167B0tIS7u7uePHiBS5evIijR4/C1tZWIakrTkdHR/xfQ6Pg98jCbpeltVVZsleovNun+P5NS0vDjRs3YGFhUWLZJk2aACjYZy1btnxtuWXFWtr7ytjYWOF9VaNGDYX5xbcTEdF/ERMsIqJ3zMTEBLm5ucjIyFBIslJTU1+7np6eHiQSCZ4+fVpiXkpKCgwMDMTXxb/wFr2PplDh/UXF6zc2Nha/OIeHh6NevXol1m3atCkMDAxKjaV4uaUpvHeoaPwAxDZoa2ujc+fO2LNnDxo0aAAtLa1Su1AW0tfXh7W1NRITE3Hz5k04OjrCxMQETZo0wcmTJ3H06FGx+195lNbWstr5JtunKD09PbRs2RKzZ88uMa/wipOurm6J909OTg4SEhJgZ2dXZh36+vpK31eVPYQ8EVFVxy6CRETvmL29PTQ0NHDgwAFxWk5ODv7666/XrlerVi2Ym5uLowkW+uuvv5CZmQl7e3sABV+2C68EFSq8F6mow4cPQxAE8fW+ffugq6uLVq1awcbGBtWqVcPTp09hZWUl/l29ehXh4eEACrr5ZWZmIiEhQSzj5s2buHPnTpnbICkpCdnZ2eLrP/74AxoaGnB0dBSn+fr64p9//sHPP/8MHx8fhStMpfHw8MDx48dx6tQpODk5AQAcHR2xa9cuXLlypdQumKpycXHBwYMHkZeXJ047dOhQmeuUd/sUZW9vj3v37sHMzEzcD5aWloiOjsaff/4pLpOUlKQwmmRCQgL8/f3x9OlTaGpqvrYOBwcH7N+/Hzk5OeK069ev48qVK+L7ioiISscEi4joHWvcuDG6du2K2bNn46effsKhQ4cwfPhwPHnypNQR7ooaPXo0zp49i8DAQBw+fBhxcXH49ttvYWdnh7Zt2wIA2rZti+TkZCxbtgzHjh3DjBkzcOHChRJlXbt2DUFBQfjrr7+wfPlybNiwASNHjoS2tjaMjIzQr18/zJ8/H6tWrcKxY8cQHR2NGTNmoGbNmtDV1YW7uzucnJwQHByM+Ph4/PHHHxg5cqTC/UPKPH/+HCNHjsTRo0cRHR2NJUuWoFevXjA1NRWXsbGxwccff4wTJ06UOnpgcR4eHjhz5gyePXsmJgVOTk44c+YM6tatC3Nz8zLLUCYgIACPHz/GyJEjcejQIaxatQobN2587Tpvsn2K6tmzJwwMDDBo0CDs3LkTf//9NwIDA7Fz506xW2D//v2Rn5+PYcOG4cCBA9i+fTumTZsGb29vNG3aFHp6egAKksLShlUPCAjAkydPMHToUBw8eBDbtm3D0KFDYWZmBl9fX7XiJSL6r2EXQSKi98D06dOho6OD0NBQ5OXl4bPPPkPHjh1x7dq1165X+Pyq8PBwjBgxAgYGBvjss88QFBQkXqX48ssvcfPmTcTGxmLt2rX45JNP8P3332PChAkKZX3++efIycnBqFGjULduXXGY8kLBwcEwMjLCpk2bsHTpUpiYmKB///4YNWoUgIKR/1asWIG5c+dizpw50NLSwqBBg7B3794y29+1a1doa2tj1KhR0NHRQf/+/TF27NgSy3l4eODFixdwdnYus0wrKysYGRmhfv36YkJReCWrMPksrxYtWmDt2rUICQnB6NGj0bRpU/zwww8IDg5Wus6bbJ+idHV1sXHjRoSEhGD69OnIyclBixYtEBERIV6V++ijjxAbG4uQkBAEBQVBT08PHTt2RFBQkBh/9+7dERkZiQsXLmDlypUKdVhaWiImJgaLFy/G2LFjxUFXgoODFQbOICKikiRC0f4gRET01qWmpuLo0aPw9PRU+PLau3dv1KlTB8uXL6/0GLy8vNC+fXvx+Ujvqy5duuCTTz4REwUiIqL3Da9gERG9Yzo6OpgxYwZ2796N3r17Q0tLC7t27cKZM2dKfdjwf40gCAgPD8c///yDu3fvKh3JkIiI6H3ABIuI6B2rWbMm1q5diyVLlmDcuHHIzc2FTCbDypUr4erq+q7De+ckEgl2796NlJQUzJ49W+G+LCIiovcNuwgSERERERFVEI4iSEREREREVEGYYBEREREREVWQKn8P1oMHD951CERERERE9IFq0KCBWsvzChYREREREVEFYYJFRERERERUQZhgERERERERVRAmWERERERERBWECRYREREREVEFYYJFRERERERUQZhgERERERERVRAmWERERERERBWECRYREREREVEFYYJFRERERERUQbTedQBEREREqqguyAFJJVciANkS/v5MROXHBIuIiIiqBgnwYqhvpVZRc3V8pZZPRB8+/kRDRERERERUQZhgERERERERVZC32kXw8OHDiI8vuPRua2sLPz8/3Lp1CytXrsTLly9hbm6OoUOHQlNT822GRUREREREVCHeWoKVnZ2NdevWISwsDLVq1cLUqVNx7tw5xMTEYNiwYZBKpVixYgX2798Pb2/vSouj0m+QfQs3x34oN/l+KO0gIqoKPoTPPyKiquCtJVhyuRyCICA7Oxs6OjrIz8+HlpYWcnJyIJVKAQDt27fHpk2bKjXBquwbZN/KzbEfyk2+H0o7iIiqgg/h84+IqAp4awlWjRo10KtXLwQGBqJ69epo1aoVtLS0YGBgIC5jaGiI1NTUtxUSERERERFRhXprCdbt27dx8OBBREREoGbNmli2bBnOnj0LieT/+ysIgqDwWhUNGjRQa/ln/z5Qa3l1aUg00MBUvZjUVdltANgOIqIPDT//VMPPDSJ6U28twTp79iwsLS2hr68PoKA74O+//45nz56Jy6SlpcHQ0FCtch88UO9kWx1ytZZXl1yQqx2Tuiq7DQDbQUT0oeHnn2r4uUFExal7Qeet3Y3auHFjnD9/Hq9evYIgCDhx4gTMzc2hra2N5ORkAAWjDNrZ2b2tkIiIiMpUXZCjOir5T6j8xIGIiN6Ot3YFy8bGBjdv3sTEiROhqamJ5s2bw9fXF87OzoiMjMTLly/RtGlTdOrU6W2FREREVDYOyENERGp4q8/B8vX1ha+v4odUkyZNMG/evLcZBhERERERUaXgAyuIiIiIiIgqCBMsIiIiIiKiCsIEi4iIiIiIqIIwwSIiIiIiIqogTLCIiIiIiIgqCBMsIiIiIiKiCqLWMO35+fnIzMyEhoYG9PT0IJFIKisuIiIiIiKiKqfMBCslJQX79u3D6dOncefOHcjlBU+b19DQQJMmTWBvbw9PT0/UqVOn0oMlIiIiIiJ6nylNsDIyMhAbG4sTJ07A2toaPj4+aNiwIXR1dSGXy/H8+XPcunULycnJCA4OhqOjI/r27Qt9ff23GT8REREREdF7Q2mCNWfOHHTq1An+/v7Q0ip9sZYtW6Jjx4549eoVDh8+jNmzZ2PhwoWVFiwREREREdH77LUJlrLEqjgdHR14e3vDy8urwgIjIiIiIiKqapRmUMqSq4sXLyIzMxNmZmZo2LChSusQERERERH9FyjNiF69eoX169fj4sWLsLKyQs+ePTF37lzcunULtWrVQlZWFpydnTFy5Ejo6Oi8zZiJiIiIqqzqghyo7IGYBSBbwqfxEL0LShOs9evX4+rVq/Dx8cGRI0cwZcoUGBkZYeXKlTA0NMSjR4+wdOlSREdHIyAg4G3GTERERFR1SYAXQ30rtYqaq+MrtXwiUk7pTxuJiYkYPnw4OnXqhMDAQDx+/Bh9+/aFoaEhAKBevXoYNGgQEhMT31qwRERERERE77PXXjvW1NQEANStWxfu7u6oUaOGwvy8vDxUq1at8qIjIiIiIiKqQpQmWA4ODli1ahVu3LgBABgzZow4qEVOTg6OHz+O8PBwuLm5vZ1IiYiIiIiI3nNKE6yBAweiXr162Lt3b4l5p06dQmhoKKytrfHNN99UaoBERERERERVhdJBLmrUqIExY8aUOs/e3h7R0dHQ1tautMCIiIiIiIiqGpXH77x27RoiIiIAANra2kyuiIiIiIiIinntk4FfvXqFv/76C3/88QdSUlLg7u5e7or279+P3bt3i68fP36Mtm3bwsnJCevXr0dOTg7c3NzQu3fvctdBRERERET0LilNsCIjI5GQkIAWLVqge/fucHZ2fqOrVh06dECHDh0AAHfv3sXChQvRvXt3TJ06FTNmzICxsTHmz5+P06dPw87Ortz1EBERERERvStKuwjevHkTmpqaaNy4MRo1alShXQKjoqLw9ddf4/HjxzA1NYWJiQk0NTXh4eGBhISECquHiIiIiIjobVJ6BWv+/Pm4efMm9u7di6lTp6J+/fpo164dOnfu/EYVnjt3Djk5OXB1dcWRI0dgYGAgzjMwMEBqaqpa5TVo0ECt5Z/9+0Ct5dWlIdFAA1P1YlJXZbcBYDuIiAp9KOcqfv6phu0gojf12nuwmjZtCn9/f/j5+eHIkSPYt2/fGydY+/btQ5cuXQAAgiBAIpEozC/+uiwPHqh3kqoOuVrLq0suyNWOSV2V3QaA7SAiKvShnKv4+acatoOIilP3go5Kowjq6Ojgk08+wfz588sVVKG8vDxcvHgRjo6OAABjY2OkpaWJ89PS0mBoaPhGdRAREREREb0rr72CVSgtLQ2///477t+/j7y8vBLzp0yZolJlt2/fhqmpKXR0dAAAzZs3x4MHD/Dw4UOYmJjgyJEj8PT0VCN8IiIiIiKi94dKCVZoaCiePHkCJyenNxrs4tGjRzA2NhZfa2trY8SIEfjxxx+Rk5MDOzs7tG7dutzlExERERERvUsqJVjXr1/HrFmz0KRJkzeqzM3NDW5ubgrTrKyssHDhwjcql4iIiIiI6H2gUoLVvHlzPH78+I0TLCJ6f1UX5IB6Y8yoTwCyJSrd+klERERUJamUYA0fPhw//PADTpw4gTp16kBDQ/ELUs+ePSslOCJ6iyTAi6G+lVpFzdXxlVo+ERER0bumUoL1888/Iz09HXfu3MHDhw8V5kkkEiZYREREREREUDHBSkpKwuTJk2FpaVnZ8RAREREREVVZKt0MUadOHXFodSIiIiIiIiqdSlewvvzyS4SHh6Nz584wMTGBpqamwnxe2SIiIiIiIlIxwQoLCwMAREVFlTo/Li6u4iIiIiIiIiKqopQmWLm5uahWrRoAJlBERERERESqUJpgBQQEoEmTJrC0tISVlRWaNWsGiaSyH5JDRERERFUBn59IVDqlCdaqVatw5coVnD9/HjExMfj3338hk8lgZWUFS0tLNGzY8G3GSURERETvEz4/kahUShMsTU1NmJubw9zcHF999RVevXqFixcv4ty5c9i7dy+ysrJgaWkJS0tLtG/f/i2GTERERERE9H5SaZALANDR0YG9vT3s7e0BAOnp6Th//jwuXLjABIuIiIiIiAgqJlgXL14sdbqxsTE6dOiAlJQU1KlTp0IDIyIiIiIiqmpUSrBWrlyJx48fQxAE1KpVC4Ig4MWLFwAKuhLm5+ejefPm+Pbbb2FoaFipARMREREREb2vVEqw2rdvj5MnT2LkyJFo0KABAODRo0dYsWIF7O3t4enpidWrV2PdunUYN25cpQZMRERERET0vlJp3MsdO3Zg6NChYnIFAPXq1cOAAQPw22+/QU9PD1999RXOnz9faYESERERERG971R+sEBGRkap0+RyeYUGREREREREVFWp1EXQy8sL4eHh6NWrFz7++GMAwI0bN/DLL7+gXbt2yMzMRGxsLCwsLCo1WCIiIiIioveZSglWnz59UKNGDWzatAnPnj0DABgaGqJjx47o1q0bzp07By0tLQwePLhSgyUiIiIiInqfqZRgSSQS9OjRAz169EBmZiY0NTVRs2ZNcb6trS1sbW3LLOfEiRPYvHkzsrOzYW1tjYEDB+LcuXNYv349cnJy4Obmht69e5e/NURERERERO+QSgnWgQMHXjvfy8urzDIePXqE1atXY+7cudDX18fMmTNx+vRprFq1CjNmzICxsTHmz5+P06dPw87OTrXoiYiIiIiI3iMqJVhbtmxReC2Xy5Geng5NTU3IZDKVEqzjx4/Dzc0NxsbGAIDAwEA8fPgQpqamMDExAQB4eHggISGBCRYREREREVVJKiVY4eHhJaa9evUKq1evhpmZmUoVPXz4EFpaWliwYAFSUlLg4OCAhg0bwsDAQFzGwMAAqampKoZeoOjQ8ap49u8DtZZXl4ZEAw1M1YtJXZXdBoDt+C/i/iAq3YdybPDzTzVsh+o+lHYQVTSVEqzS6Ojo4Msvv8SUKVPQo0ePMpfPz8/HpUuXMH36dOjo6GDBggXQ1taGRCJRWK7467I8eKDewV0dlTusvFyQqx2Tuiq7DQDb8V/E/UFUug/l2ODnn2rYDtV9KO0gKou6F3TKnWABwNWrV1V+DpaBgQGsrKxQu3ZtAICzszOOHTsGDY3/fxRXWloaDA0N3yQkIiIiIiKid0alBOuHH34ocWXpxYsXuHPnDrp166ZSRQ4ODggPD0dWVhZq1KiB06dPw8XFBb/99hsePnwIExMTHDlyBJ6enuq3goiIiIioHKoLckC9DlTqE4BsiUbZy9EHQaUEy8rKSuG1RCKBlpYW/Pz8SsxTpkWLFujWrRt++OEH5OXlwdraGt7e3jAzM8OPP/6InJwc2NnZoXXr1uq3goiIiIioPCTAi6G+lVpFzdXxlVo+vV9USrC+/PLLCqnMy8urxIiDVlZWWLhwYYWUT0REREREVdOHcjVRaYI1depUfPnll7C2tlapoFOnTuHXX3/FrFmzKiw4IiIiIiL6j/hAriYqTbACAgKwdu1arFmzBk5OTrC2toaZmRn09PQgCAIyMzNx+/ZtXLp0CQkJCTAxMcGwYcMqPWAiIiIiIqL3ldIEy8zMDFOnTsXly5exd+9eLF26FJmZmQrL1K5dGzY2Nhg9ejRatmxZ6cESERERERG9z8q8B0smk0EmkwEAnjx5gvT0dEgkEhgYGMDY2LjSAyQiIiIiIqoq1HoOVt26dVG3bt3KioWIiIiIiKhK44D8REREREREFYQJFhERERERUQVhgkVERERERFRB1LoHCwAEQYAgCArTNDSYpxEREREREamUYN24cQNr1qzBjRs3IJfLS8yPi4ur8MCIiIiIiIiqGpUSrKioKFSrVg3jx49HzZo1KzsmIiIiIiKiKkmlBOvu3buYM2cOGjVqVNnxEBERERERVVkq3TzVpEkTPH78uLJjISIiIiIiqtJUuoLl7u6OyMhIJCcnw8TEBFpaiqt5eXlVSnBERERERERViUoJ1u+//w5tbW0kJCSUmCeRSJhgERERERERQcUEKzw8vLLjICIiIiIiqvJUfg7W06dPsWvXLty/fx9yuRxmZmbo0KEDzMzMKjM+IiIiIiKiKkOlQS4uXryIwMBAJCcnw9TUFKamprh8+TImTJiA5OTkyo6RiIiIiIioSlDpCtaGDRvQqVMn9OnTR2H6Tz/9hNjYWMyePbtSgiMiIiIiIqpKVH4O1tixY0tM9/T0xM6dO1WubMaMGUhPT4empiYAwN/fH48ePcKWLVuQn5+Pzp07o2PHjiqXR0RERERE9D5RKcEyMTHB1atXUb9+fYXpV69ehb6+vkoVCYKABw8eICIiQkywUlNTERoaigULFkBLSwtTp06FpaUlGjZsqGYziIiIiIiI3j2VEqxu3bph1apVuHv3Lpo3bw4AuHbtGvbs2VOi26AyDx48AADMnj0bz58/R4cOHaCjowNLS0vo6uoCAFxcXHDs2DH07NmzPG0hIiIiIiJ6p1RKsNq3bw8A2LVrF3bu3AltbW2YmZlhxIgRcHFxUamirKwsWFlZYdCgQcjLy8OMGTPg6uoKQ0NDcRlDQ0Ncu3ZN/VYQERERERG9B1Qepr19+/ZiolUeUqkUUqlUfO3p6Yn169ejR48e4jRBECCRSNQqt0GDBmot/+zfB2otry4NiQYamKoXk7oquw0A2/FfxP1BVLoP5djg559q2A7VsR2q4+efaj6UfaE0wYqLi4Ovry+qV6+OuLi41xbSq1evMitKTk5Gbm4urKysxGl169ZFWlqa+DotLQ1GRkaqxC0q7HqoquqQq7W8uuSCXO2Y1FXZbQDYjv8i7g+i0n0oxwY//1TDdqiO7VAdP/9U877uC3Uv6ChNsJKTk5GXl4fq1atXyLOuskIWJ0EAACAASURBVLKysGnTJsyaNQv5+fk4dOgQRo8ejWXLliEjIwPVq1dHYmIihg0b9sZ1ERERERERvQtKE6xp06aJ/48cORJGRkbQ0FB8LrFcLsft27dVqsjBwQFXr17Fd999B7lcDh8fH7Rs2RJff/01ZsyYgby8PHh5eYmDaBAREREREVU1Kt2DNXLkSKxevRq1a9dWmP748WNMnToVsbGxKlXWu3dv9O7dW2FamzZt0KZNGxXDJSIiIiIien8pTbD27duHLVu2iK8nTJhQYgCKFy9e4KOPPqq86IiIiIiIiKoQpQlW+/btUa1aNQiCgBUrVqBbt26oWbOmOF8ikYjPsSIiIiIiIqLXJFhaWlpo164dAMDExAQymQyamppvLTAiIiIiIqKqRqV7sFq2bInjx4/j7t27kMsLhk8UBAF5eXm4efMmpk6dWqlBEhERERERVQUqJVhr167FwYMH0bRpU1y7dg0ymQwPHz5EWloafHx8KjtGIiIiIiKiKkGlBCshIQFjxoyBi4sLAgMDMWTIEJiZmWH58uXIycmp7BiJiIiIiIiqBI2yFwFevnyJZs2aAQAaNWqEq1evQkNDA59//jlOnz5dqQESERERERFVFSolWPXq1cPNmzcBAA0bNsS1a9cAFNyH9fLly8qLjoiIiIiIqApRqYtgt27dEBYWhuHDh8PNzQ3fffcdJBIJrly5gpYtW1Z2jERERERERFWCSgmWp6cnTE1Noa2tjYYNGyI4OBgHDhxAixYt8NVXX1V2jERERERERFWCSgnW7NmzMWDAADRs2BAAYGtrC1tb20oNjIiIiIiIqKpR6R6sW7duQUtLpVyMiIiIiIjoP0ulrOnTTz/Fjz/+iE8//RR16tSBtra2wnxLS8tKCY6IiIiIiP6vvXuPq6rO9z/+3oDcUgQxBbxVJopuQM1SMRuPZsembKabmaVpljlS09Uuk6b1KNPInDNphmamjZp283T5OTVSpiZeUEFBEbGYRKDUDSUgctn794cP95EUWxvBzVq9no9Hj/a6uNbn42Kx/KzvZcNMDBVYH330kSRp0aJFZ92+cuXKhosIAAAAAEzKUIFFAQUAAAAAv83QGKwHH3xQpaWlZ6x3OBy67777GjwoAAAAADCjOluwNm3apB07dkiSDh8+rAULFpwx9urw4cPy9fVt3AgBAAAAwCTqbMGy2+3y8fGRj8/JXU59Pv2/Tp066cknn7xgwQIAAABAU1ZnC1ZISIgmTZokSbr44os1fPhwBQYGXrDAAAAAAMBsDE1ycfvtt6usrEzZ2dmqrq4+YzvTtAMAAACAwQJr3bp1WrRokSorK8+63ZNZBpcuXapjx44pMTFReXl5evPNN3X8+HHFxMTo/vvvZ0wXAAAAANMyVGB9/PHH+q//+i/deeedCgoKqvfJdu/erW+++Ua9e/eWJL3++ut64IEHFB0drfnz5yslJUXXXXddvY8PAAAAAN5kaJp2h8Oh66+//ryKq9LSUr333nu6+eabJZ2cgbCyslLR0dGSpEGDBik1NbXexwcAAAAAbzPUgtWnTx+lpaVp+PDh9T7RggULNHLkSB09elSSVFxcrNDQUPf2sLAwORwOj48bFRXl0f7FhQUen8MTPjYfRUV6FpOnGjsHiTx+j7gewNlZ5d7g+WcMeRhHHsbx/DPGKtfCUIHVsmVLvffee9q0aZPatm0rP7/af+zBBx88559PSUlReHi4YmNjtW7dOkmS0+mUzWZz7+NyuWotG1VQ4NmFCJDT43N4wulyehyTpxo7B4k8fo+4HsDZWeXe4PlnDHkYRx7G8fwzpqleC08bdAwVWOXl5RowYIBHBz7dpk2bVFJSosmTJ6u0tFQVFRWy2WwqLi5271NSUqKwsLB6nwMAAAAAvM1QgXXq+7Dqa+rUqe7P69atU1ZWliZNmqTHH39c2dnZ6tatm9avX69evXqd13kAAAAAwJsMFViStGPHDn3++ecqKirS9OnTlZKSotatW+vaa6+t98kfeughJScn6/jx47r00kt1/fXX1/tYAAAAAOBthgqs9evXa/Hixbrhhhu0b98+OZ1OhYaGasmSJaqoqNCNN95o+ISDBg3SoEGDJEmXXHKJXn755XoFDgAAAABNjaFp2v/3f/9XEyZM0G233SYfn5N/ZNiwYUpMTNSaNWsaNUAAAAAAMAtDBdaPP/6ozp07n7H+kksuUUlJSYMHBQAAAABmZKjA6tixo3bs2OFePjWd+ldffaVOnTo1TmQAAAAAYDKGxmCNGTNGL7/8snbv3q3q6mp98MEHKiwsVF5enp566qnGjhEADAlwOSXPv07PMy7phM3QuykAAPA7ZKjA6tatm/7nf/5HX3zxhXx9fVVWVqZu3brp4YcfVuvWrRs7RgAwxiaV3//nRj1F8MLVjXp8AABgboanaa+srFT//v3VsWNHSSe7B7pcrkYLDAAAAADMxlA/l+3bt+uxxx5TWlqae92mTZv0+OOPKzMzs9GCAwAAAAAzMVRgLV++XKNGjdItt9ziXjdlyhSNHDlS7777bqMFBwAAAABmYqjA+umnn9S7d+8z1vfu3VuHDh1q8KAAAAAAwIwMFVjt27fXt99+e8b6LVu2KCIiosGDAgAAAAAzMjTJxZ133qmZM2dq165duvTSSyVJeXl52r9/vyZPntyoAQIAAACAWRgqsOLi4pSUlKSvvvpKhw4dkp+fny6//HJNmjRJbdq0aewYAQAAAMAUDE/T3q5dO40ePboxYwEAAAAAUzNUYJWWluqTTz7RgQMHVF1dfcb2559/vsEDAwAAAACzMVRgvfHGG9q/f78GDhyooKCgxo4JAAAAAEzJUIG1Z88ePf300+rWrVtjxwMAAAAApmVomvaQkBA1a9assWMBAAAAAFMz1IJ111136a233tLtt9+utm3bys+v9h9r27ZtowQHAAAAAGZiqMB67bXXJEmzZs066/aVK1c2XEQAAAAAYFKGCqy5c+c2yMlWrlypzZs3y2azafDgwbrxxhu1a9cuLV26VJWVlUpISNDIkSMb5FwAAAAAcKEZKrAuvvhiuVwupaen69ChQ3I6nYqKilLPnj3P6C5Ylz179igzM1Ovvvqqampq9Oijj8put2v+/Pl6/vnnFR4erpkzZ2rnzp3q1avXeSUFAAAAAN5gqDo6cuSIZs2apaKiIkVFRcnpdKqoqEjh4eF67rnn1KpVq988Rvfu3TVt2jT5+vrK4XDI6XSqvLxckZGRatOmjSRp4MCBSk1NpcACAAAAYEqGCqxFixapVatWmjZtmpo3by5JOnbsmObOnavFixfr8ccfN3YyPz+tWrVKn376qfr16yeHw6HQ0FD39tDQUDkcDo8SiIqK8mj/4sICj/b3lI/NR1GRnsXkqcbOQSKP3yMrXA8r5ICmxyo/Vzz/jCEP48jDOJ4dxljlWhgqsDIzMzVjxgx3cSVJLVq00F133aWpU6d6dMIRI0boT3/6k2bNmqXCwkLZbLZa23+9/FsKCjy7EAFyerS/p5wup8cxeaqxc5DI4/fICtfDCjmg6bHKzxXPP2PIwzjyMI5nhzFN9Vp42qBj6HuwmjdvrmPHjp2x/tixY4bHYB06dEh5eXmSpICAAF111VXas2ePSkpK3PuUlJQoLCzM0PEAAAAAoKkxVGBdffXVSk5OVkZGhsrKylRWVqb09HQtWLBAAwYMMHSiH3/8UcnJyaqqqlJ1dbXS0tJ07bXXqqCgQEVFRXI6ndq4cSPjrwAAAAAPBLicClAj/+dq/NYlqzDU/DRixAiVlJTo5ZdflsvlkiT5+Pho6NChuvvuuw2dqHfv3srNzdWTTz4pHx8f9e3bVwMGDFBISIhmz56tyspK9erVS/369at/NgAAAMDvjU0qv//PjXqK4IWrG/X4VmKowGrWrJkSExM1duxYFRYWqlmzZoqIiFBAQIBHJxsxYoRGjBhRa11sbKySkpI8Og4AAAAANEWGCqyKigotXbpUkZGRGj58uCTp4YcfVlxcnEaPHi1/f/9GDRIAAAAAzMDQGKy33npLBw4cUPfu3d3rxo8fr5ycHC1ZsqTRggMAAAAAMzFUYO3cuVOJiYnq3Lmze11cXJwmTpyoLVu2NFpwAAAAAGAmhgosm82m48ePn7G+urpaNTU1DR4UAAAAAJiRoQKrf//+Sk5OVmZmpsrLy1VeXq6srCwtWLBAffv2bewYAQAAAMAUDE1yMXr0aCUnJ+ull16S03lyDnwfHx8NGjRI99xzT6MGCAAAAABmYajA8vf310MPPaTx48ersLBQfn5+atu2rQIDAxs7PgAAAAAwDUMF1inBwcG1JroAAAAAAPwfQ2OwAAAAAAC/zaMWLABnCnA5JVsjn8QlnbDxPgQAAKCpo8ACzpdNKr//z416iuCFqxv1+AAAAGgYdRZYaWlp6tmzp/z8/m+X3NxcrV27Vg6HQ1FRUfrjH/+oNm3aXJBAAQAAAKCpq7PPUVJSksrKytzLO3fu1JQpU/Tzzz+rY8eOys/P1+OPP67s7OwLEigAAAAANHWGuwi+//77uvXWW3X77be7161atUpLly7VjBkzGiU4AAAAADATw6Pmjx49qr59+9Zad8011+iHH35o8KAAAAAAwIzOWWDt3btXR44ckSR169btjGIqNzdXrVq1arzoAAAAAMBE6uwiGBsbqyVLlsjhcMjf31/BwcFKT09X7969FRwcrLfeekvr1q3T6NGjL2S8AAAAANBk1VlgTZkyRZJ0/Phx5efnKz8/X4cOHVJQUJAk6ZdfftGECRN0zTXXXJhIAQAAAKCJ+81JLoKCgtSlSxd16dKl1vrHHnus0YICAAAAADMyPMnF2VRWVuqbb75pqFgAAAAAwNQMT9N+NuXl5XrjjTf0hz/8wdD+77//vlJTUyVJvXv31t13361du3Zp6dKlqqysVEJCgkaOHHk+IQEAAACA15xXC1ZoaKhWrlxpaN9du3Zp165deuWVV/TKK6/ou+++08aNGzV//nw9+eSTmjNnjg4cOKCdO3eeT0gAAAAA4DXnVWBJktPpNLRfWFiYRo8eLT8/P/n5+aldu3YqLCxUZGSk2rRpI19fXw0cONDdwgUAAAAAZlNnF8Hq6mqtWLFCGzduVFVVlWJiYjRq1Ci1a9fOvU9JSYkeeOABQ61YHTp0cH8uLCxUamqqhg0bptDQUPf60NBQORwOjxKIioryaP/iwgKP9veUj81HUZGexeSpxs5BIg9PkIdxjZ2HFXJA02OVnyuef8aQh3HkYRzPP2OskkedBdby5cu1bds29/dcffHFF3r66af1yCOP6Iorrqj3CQ8ePKiZM2fq7rvvlq+vrwoLC2ttt9lsHh2voMCzCxEgYy1u9eV0OT2OyVONnYNEHp4gD+MaOw8r5ICmxyo/Vzz/jCEP48jDOJ5/xjTVPDxt0Kmzi2BqaqomTZqkq6++WldffbVeeOEFDRkyRLNnz9a2bds8Oskp2dnZeuGFFzRq1CgNGjRI4eHhKikpcW8vKSlRWFhYvY4NAAAAAN5WZ4FVUVGhli1bupdtNpvGjh2roUOH6u9//7vHk1EcOXJESUlJevjhhzVgwABJ0uWXX66CggIVFRXJ6XRq48aN6tWrVz1TAQAAAADvqrOLYLdu3bRy5UolJibK39/fvX7cuHE6fvy4Xn31VY0YMcLwiT799FNVVVVpyZIl7nVDhw7VpEmTNHv2bFVWVqpXr17q169fPVMBAAAAAO+qs8AaO3asZs6cqXHjxumZZ56R3W53b/vLX/6i4OBgLV++3PCJxo0bp3Hjxp11W1JSkgchAwAAAEDTVGeB1bZtWyUlJSk3N/eMgV2nugsmJCRoy5YtjR4kAAAAAJhBnQWWJPn5+albt251bo+OjlZ0dHSDBwUAAAAAZlRngfWXv/zF8EHmz5/fIMEAAAAAgJnVWWD9/PPPqqmpUXR0tPr06VNrRkEAAAAAwJnqLLAWLVqknTt3auvWrVq9erU6dOigq666SldddZXatGlzIWMEAAAAAFOos8AKCgpSQkKCEhISVF1draysLG3btk3PPfecWrRo4S62OnXqdCHjBQAAAIAm65yTXLh38vNTfHy84uPjdd999yk3N1dbt27VtGnT1KJFC73++uuNHScAAAAANHmGCqxTTpw4oYyMDKWlpWnnzp3y8/NTTExMY8UGAAAAAKbymwXW0aNHtX37dqWlpSkrK0vh4eHq06ePHnvsMXXt2lU+Pj4XIk4A+N0IcDklWyOfxCWdsDXu72+r5AEAgCfqLLBWrVql7du364cfftBll12mK6+8UmPGjFH79u0vZHwA8Ptjk8rv/3OjniJ44epGPb4k6+QBAIAH6iywPvzwQ/n5+al79+4KDQ1Vfn6+8vPzz7rvgw8+2GgBAgAAAIBZ1FlgXXPNNbLZGrtvBwAAAABYR50FVmJi4oWMAwAAAABM75yTXGzYsEFbtmyRn5+frrzySg0YMOBCxQUAAAAAplPn1EurV6/WG2+8ocrKSlVUVGjevHlavnz5hYwNAAAAAEylzhaslJQUTZw4UX/4wx8kSVu2bNEbb7yhO++8k7FZAAAAAHAWdbZgHTlyRLGxse7lPn366MSJEyouLr4ggQEAAACA2dRZYDmdTvn6+rqXfX195e/vr6qqqgsSGAAAAACYTZ0FFgAAAADAM+ecRfDbb79VYGCge9npdCo1NVUhISG19hs8eLDhE5aXl2vq1Kl66qmn1KZNG+3atUtLly5VZWWlEhISNHLkSA9TAAAAAICmoc4Cq3Xr1vr8889rrWvZsqX+/e9/11pns9kMF1j79+9XcnKyCgoKJEmVlZWaP3++nn/+eYWHh2vmzJnauXOnevXq5WkeAAAAAOB1dRZY8+bNa/CTpaSkaPz48Zo7d64kKTc3V5GRkWrTpo0kaeDAgUpNTaXAAgAAAGBK5+wi2NAmTpxYa9nhcCg0NNS9HBoaKofD4dExo6KiPNq/uLDAo/095WPzUVSkZzF5qrFzkMjDE+RhXGPnYYUcJPLwBHkYx/PPGPIwjjyM4/lnjFXyuKAF1q+5XK4zvlPL0+/YOtXd0KgAOT3a31NOl9PjmDzV2DlI5OEJ8jCusfOwQg4SeXiCPIzj+WcMeRhHHsbx/DOmqebhaYOOV2cRDA8PV0lJiXu5pKREYWFhXowIAAAAAOrPqwXW5ZdfroKCAhUVFcnpdGrjxo2MvwIAAABgWl7tIujv769JkyZp9uzZqqysVK9evdSvXz9vhgQAAAAA9eaVAuv0GQpjY2OVlJTkjTAAAAAAoEF5tYsgAAAAAFgJBRYAAAAANBAKLAAAAABoIBRYAAAAANBAKLAAAAAAoIFQYAEAAABAA6HAAgAAAIAGQoEFAAAAAA2EAgsAAAAAGggFFgAAAAA0EAosAAAAAGggFFgAAAAA0EAosAAAAACggVBgAQAAAEADocACAAAAgAZCgQUAAAAADYQCCwAAAAAaCAUWAAAAADQQCiwAAAAAaCB+3g5AkjZu3KgPP/xQNTU1+uMf/6hhw4Z5OyQAAAAA8JjXCyyHw6EVK1Zo1qxZ8vPz09SpU2W329W+fXtvhwYAAAAAHvF6F8Fdu3bJbrerefPmCgwMVN++fbV582ZvhwUAAAAAHvN6gVVcXKywsDD3clhYmI4ePerFiAAAAACgfmwul8vlzQA++ugjVVZWauTIkZKktWvX6rvvvtOECRO8GRYAAAAAeMzrLVitWrVSSUmJe7mkpEStWrXyYkQAAAAAUD9eL7Di4uK0e/du/fLLLzpx4oS2bNminj17ejssAAAAAPCY17sISienaf/4449VXV2twYMH609/+pO3QwIAAAAAjzWJAgsAAAAArMDrXQQBAAAAwCoosAAAAACggVBgAQAAAEADocACAAAAgAZCgQUAAAAADYQCCwAAAPiVI0eOeDsEmBTTtJ/myy+/1HXXXSdJOnjwoDp06ODe9s4772js2LFeigxm9Vu/nFu3bn2BIoGVHDlyxPQ/O1bI4ZTt27erffv2atu2rbZu3aqvvvpKl156qW699Vb5+fl5O7zzUl5eruDgYG+HAXjFAw88oMDAQMXHxysuLk52u12BgYHeDgsm4Dt9+vTp3g6iqViwYIGGDh0qSZoxY4b7syStWLGi1nJTNn/+fF155ZWSpHXr1umSSy5xb5s6daoGDx7spciMczgcCgoKOuu2zMxMtWnT5gJHVD+TJ0/WN998o6+//lrr1q3TunXr9M033+jjjz/WZ599pttvv93bIRry5ZdfqnPnzpJOvnxo2bKle9s777yjnj17eis0j1glj0cffVRr165VYWGhampq1KpVK9P9Q94KOUjSJ598oi+//FJXXHGFiouLNXPmTN1www366aeflJmZaYqfqRkzZmjgwIGSpI8//lgxMTHubVOmTDHNsy83N1etWrU667b169erU6dOFzii+klMTNShQ4dUVVWlsLAw+fv7ezukevnggw/k5+ensLAw2Ww2b4dTL8OHD1evXr1UU1Oj9PR0rVq1SqmpqTp69Kj8/PwUHh7u7RANscK1kMx1b5jvadaITm/MM3PDXl5envvzmjVrNGjQIPfyiRMnLnxA9TBr1izNmjVLkvTqq6/qiSeecG9799133duaunnz5tVarqio0NKlS5WRkaEHHnjAS1F5LiUlxd26O3fu3Fp//3v37vVWWB6zSh7JyckqKipSdna2tm7dqmXLlikkJESxsbGKi4tTdHS0t0P8TVbIQZI2bNigF198UQEBAVq2bJmuuOIKDRkyRC6XS4899pi3wzOkpKTE/Xnz5s26+eab3ctmehYuXLjQfU8/++yzeumll9zbPv/8c11zzTXeCs0jzz77rLKzs7V9+3YtX75cLVq0UGxsrOLj4xUdHS1fX19vh2hIZWWl/vnPf6qwsFBdu3ZVXFyc4uLiFBER4e3QPBIREaGIiAgNGjRIZWVl2rZtmz777DN99NFHWr58ubfDM8Qq18JM9wYFVh3MXOGfq1A0S16nx/3TTz/Vuc1Mdu/ereTkZMXFxenVV1+ts4WuKbLKywer5CFZ46FvhRwkKSAgQJKUlZXlLuDN8rtWqh2rWZ8ZUu3Yq6qq6tzW1EVFRSkqKsrd28ThcCg9PV2LFi3S4cOHtWTJEi9HaMyoUaMknbwW+/fvV3Z2tt5++20VFxcrOjpa999/v5cj/G01NTXKzs5Wenq6MjIyVFlZqdjYWI0YMUJ2u93b4RlmhWshmeveoMA6jZkeJOdyeh5mzelcOZgtp1+3WsXFxXk7pPNitr//upg5Dys89K2QgyT5+vqqrKxMFRUV+v777xUfHy9JOnz4sHx8zDePlJnvCys9NyorK7Vnzx5lZGRoz549cjqdstvtpnx+NGvWTBdddJECAwN10UUX6eeff1Zpaam3wzJk3Lhx6tq1q/r27asnnnjCNMMT6mLma3GKWe4NCqzTHDx4UA8++KCkk1Xxqc8ul6tWF4qmrrq6WkeOHJHL5XJ/Pn0bLpzTW61mz55t2sGxZvvHSV2skocVHvpWyEGS/vznP+vJJ59UTU2NhgwZorCwMG3atEkrVqwwzRjL48ePa+/evXK5XKqoqNCePXvc2yoqKrwY2e/TSy+9pIKCAndXruHDh9c5tqwp27hxozIyMpSVlaW2bdsqNjZWN9xwgzp37mya38XXXnutMjMz9fXXX8vhcLi7L5vt5YkVroVkrnuDWQRPc/jw4XNuv/jiiy9QJOcnMTGxzm02m01z5869gNHUz4QJE9xdbU6f3dHlcmnt2rVKTk72ZniG3XHHHfL19T1jYKnL5TLNtZCkO++8U+Hh4XK5XCouLnb/Qjv18mHZsmVejtCYUaNGuWN3OBymzWPp0qXKzMxUs2bN3LNbme2hb4UcTnE4HDp27Jh7EoUdO3YoICBAPXr08HJkxkyfPv2c/8iaNm3aBYym/u6991716dNHkpSWlub+LJ2c6XHRokXeCs0jTz/9tGw2m+x2u+Lj49WtWzdTTgBzxx13KD4+XnfccYd7ciGzKi4uVkZGhtLT03XgwAF17NhR8fHx7n+bNHVWuRZmujcosNAkvf/+++fcbpY3w1Yp2s+Vh81mM81021a5HqeY/aEvWSMHK0hLS1P79u0VERFh2qnm161bd87tp0/41NQdO3ZMGRkZysjIUHZ2tqKiotwvItq3b+/t8Aw5ePCgO4effvpJMTExio+PV2xsrJo3b+7t8DxWXV2t3Nxc7du3T+vXr1dpaalpXvZa6VqY5d6gwLKgb775ps5tNpvNNDMpoelITEw86xvu4uJiVVdXa+XKlV6IynNW/F4yMz/0T7FCDmb26aefatOmTUpMTFRNTY2mTJmisWPHKi8vT76+vqb+Dshjx46pefPmpuoG9WvV1dVav369Pv/8c+Xn55vm9+3pTo2b2bVrlzIyMhQQEKAZM2Z4O6zflJaWpuzsbO3bt08//vijunTpIrvdLrvdXuu7Us3ErNfibJryvWGO11LwSFZW1hnrqqurtWXLFgUGBpqiwKqsrNR7772nhIQEXX755VqyZIlSUlJ06aWX6uGHH26yfW6tyirTzU+bNk02m63WjGI2m810hWJdD/1HHnnENA99K+RgFevXrzf9VPOS9Msvv2jhwoUaNmyYYmJi9NprrykjI0OhoaF66qmnmtTb7XMpKytTTk6O9u3bp3379qmoqEidO3fW0KFDFRsb6+3wPHbq6xiys7N14MABBQYGqnv37t4Oy5B//etfstvtuueee3TZZZeZsgvz6cx8LSRz3RsUWBY0adKkWsvfffed3njjDfXs2dM0U3EuXrxYvr6+uvjii7Vjxw5t3LhRs2bN0vfff69FixZp8uTJ3g7xd8vM081bpVC0wkP/iy++UI8ePTR27Fg1b9681veXHDlyxJStiWZm9qnmJentt99W586d1blz6F6l9AAADr1JREFUZ23evFnff/+9kpOTlZ+fr8WLF2vq1KneDtGQSZMmqWvXrrLb7Ro9erQuvfRS010LSUpKSlJOTo5CQkLUo0cP9e7dW2PGjFFwcLC3QzNsypQp3g6hQZztWowePVoXXXSRt0PziJnuDQosC6upqdH777+vr776SmPGjNHVV1/t7ZAMy8nJ0ezZsyWdfNPdv39/RUZGKjIy8jfHZ6FxWG26eTMXihMnTnR/djgcZ2w3Q3FSUFCggoICrV27ttZ6s7UmWoFVpprPz8/XI488IknauXOn+vfvr+DgYEVHR6u4uNjL0Rm3aNGiM8a9mbGrY//+/XXVVVepa9eu7rF9//jHP3TZZZfplltuMc3YPivo37+/7r//foWGhno7lPNipnuDn26L+v777zVv3jxFRETolVdeMd1NdfpDPSsrS3fffbd7manmLzyrTDcvWaNQtEJXR6u0JlqBFaaal2q3uGVmZtZ6EXHixAlvhFQv5eXltbo6zp49W7t27TJdV0eHw6HU1FR17txZ//nPf/T666+7x/b985//NPXYPrMpLCxUUVFRndtvu+22CxhN/Znp3qDAsqD33ntP/+///T/dfPPNGjhw4BnfhWWGt9stWrRQbm6uKioq5HA43H1rs7KyFB4e7uXofn9efPFF+fr6KiMjQ0888YR7vdmmm7dKoWi14sTMrYlW0K9fP0VHR9eaaj4wMFATJ040zVTz0sln26ZNm3TixAlVVla6Y1+/fr2pxvX9uqtjXl6eKbs6btiwwRJj+6zi13PalZaWau3atWrdurVpCiwz3RsUWBa0YcMGtWjRQmvXrlVKSsoZb7nN8I/hsWPHas6cOfr555913333KTAwUB9++KHWrFmjp59+2tvh/e6Y4WfGCKsUiqczc3FihdZEq2jVqlWtyYN69+7txWjqZ/z48Vq4cKFKSkr00EMPyc/PT0uWLNH27dv1zDPPeDs8w6zS1VGyxtg+K/h1S3RaWpreeustDR06VKNGjfJSVJ4z071BgWVBN910k/77v/9bkvTDDz+oY8eO7m2LFy/2Vlgeyc7O1pw5cySd/P4GSRowYICGDRum999/X5dffrk3w/vdMdv3Q9Vl7ty57of7r9/mme2hb/bixCqtiWg6WrduraFDh9b6Pq+CggL179/fVL/DrNLV0Spj+6ykrKxMb7/9tnJzc/XII4+oW7du3g7JI2a6NyiwLOirr75yF1jz5s3TrFmz3Nuys7O9FZZHUlJS3G+75s6dq1mzZikiIkKStHfvXm+GBhObPn26Jb7PywrFiRVbE+Fdn3zyiVJTU5WYmGjqMT9W6epolbF9VpGWlqZFixapX79+SkpKkr+/v7dD8tipe6OioqLJ3xsUWBZ0+pv5X7+lN8v3SlshBzQ9Vhm7ZIXixAwxwlysMubHKl0drTK2zwpef/11bd68Wbfeequ6deum3NzcWtvN8l1YN910k1avXq2SkhL99a9/dd8baWlp+tvf/ubt8GqhwLK4X7+tN1s3KMkaOaDpMfPYJSt0dTRTly2YhxXG/Filq6NkjbF9VpCdna3o6Gjt3r1bu3fvPmP7tGnTvBCV5/7+978rMDBQ8fHxqqqqUkVFhW677TaNHj26yXU7pcCyILM9TM7GCjmgaTL72CXJOl0dgYZklTE/VunqiKajurpaDodD8fHxiouLk91uN2XX8uTkZBUVFSk7O1tbt27VsmXLFBISori4OMXFxalLly7eDtHN5qK/leWMGjXK/cbI4XC4P7tcLpWUlGjZsmXeDM8QK+SApuf0VqsxY8aY8gFzNlYoGoHztXnzZr377ruqqalRnz59dN9999Ua83PNNdd4O0RDJk+eXKur4+HDh/XII4+4uzqemgAK8MSpwmTPnj3av39/ky1MPFFWVqZt27bps88+U0FBgZYvX+7tkNwosCzo8OHD59xuhi4GVsgBTc8dd9whX19fhYWF1WoBMtPYpV87vWgcPXq0qbo6Ag3N4XDUGvOzY8cOBQQEmGrMz+TJk5WUlCRJ+tvf/qbrrrtOgwYNkiQ9+uijFFg4b025MDmXmpoaZWdnKz09XRkZGaqsrFRsbKxiY2Nlt9sVHBzs7RDd6CJoQVYoPqyQA5oeMxZQdaHVCjiTFcb8WKWrI5qOugqTESNGyG63ezs8w8aNG6euXbuqb9++euKJJ9SmTRtvh1QnWrAAwGSs2tURgHW6OqLpGDNmjLswiYuLa9KFybksXbpUmZmZatasmXs8WXR0dJN88UCBBQAmY8WujgD+jxW6OqLpMFNhYkRxcbEyMjKUnp6uAwcOqGPHjoqPj3fPHNoUUGABgMkwRhEA4CkzFCZGVVdXKzc3V/v27dP69etVWlqq5ORkb4flRoEFAAAA/A409cLkXNLS0pSdna19+/bpxx9/VJcuXWS322W329WhQwdvh1cLBRYAAABgUWYqTM7lxRdfdMd92WWXNekujhRYAAAAgEWZqTCxCgosAAAAAGgglLAAAAAA0EAosAAAAACggVBgAQDq7eWXXz7je7d27typESNG6J133qm1fu3atbr33nvlcrmUmJiolJSUep0zJSVFiYmJhvefOnWqVq1aJUmaN2+e/vGPf9TrvL8lLy9Pe/fulSRlZWVpxIgRqqmpaZRzAQCaLj9vBwAAMK+YmBh9/fXXtdZlZmYqLCxMmZmZtdbv379fMTExstlsevnllxUYGHghQ5UkjRs3rtGOnZSUpFtuuUUxMTHq2rWrFixYIF9f30Y7HwCgaaIFCwBQbzExMSoqKlJZWZl7XVZWloYPH66DBw/ql19+ca/PyclRjx49JEkhISHy9/e/4PEGBwcrODi40c/j5+en0NDQRj8PAKDpoQULAFBvnTt3VrNmzXTgwAHFxcWprKxMeXl5euaZZ/Tll18qMzNTCQkJKi8vV0FBgbp37y5JSkxM1C233KIhQ4Zo+vTpstvt2rdvn/bu3au2bdvqrrvuUu/evSVJDodDb775pvbu3at27dopPj7+nDFt3bpVy5Ytk8Ph0ODBg3X6ZLnz5s1TTU2N/vrXv2rVqlX6/vvvVVFRoby8PD388MPq0aOHli1bpg0bNsjlcslut+vee+91F0s//fST3n77be3Zs0fBwcG67rrrdMstt2j69Ok6fPiwkpOTlZ2drUGDBun555/XihUr5Ovrq6NHj2rJkiXavXu3fHx8lJCQoNGjR8vf31/r1q1TSkqK4uPjtWbNGvn6+iohIUFjxoxhOmUAMCF+cwMA6s3Pz09dunTR/v37JZ1svWrXrp1atmyp7t27u7sJ5uTk6KKLLlKnTp3OepzVq1drwIABmjlzptq1a6fk5GQ5nU5J0muvvSan06kZM2bopptu0po1a+qMJz8/X3PmzNHQoUM1c+ZMVVVVKScnp879t2/frr59+2r69Onq2rWrVqxYoZycHD399NOaPn26XC6XZs2aJZfLpaqqKr344ovy9fXViy++qIkTJ+qTTz7Rhg0b9MQTTyg8PFxjxow5oxtidXW1XnjhBZ04cULTp0/Xo48+qvT0dC1dutS9T25urvLz8/XCCy9o5MiRWrNmjTIyMoxdBABAk0KBBQA4LzExMcrNzZV0ssA61Uplt9uVlZUlqfb4q7Pp2bOnBg0apPbt2+vWW29VcXGxHA6HDh48qJycHE2YMEEdOnRQQkKCrr322jpj+frrr9W1a1fdeOONateuXa3Wp7Np0aKFhg0bpk6dOsnHx0f/+te/dP/996tLly7q2LGjHnroIR08eFDZ2dnavXu3iouLlZiYqI4dO6pnz54aP368AgIC1Lx5c/n4+CgoKOiMLojp6ek6evSoHnroIXXq1El2u13jx4/X2rVrVV5eLklyOp2aMGGC2rVrp8GDB+uSSy7RgQMHjF8EAECTQRdBAMB5iYmJ0b///W9JJye4uP322yVJ3bt3V2FhoX7++Wfl5OSoV69edR4jIiLC/TkoKEjSyZaf/Px8BQUFqU2bNu7tnTt31pYtW856nPz8/FqtZH5+fnW2mknSxRdf7P78448/qrq6Ws8991ytfaqqqlRYWKjS0lJFRETUKqAGDhxY57FPjykiIkLNmzd3r4uOjpbT6VRhYaGkk4Xe6ccNCgpSdXX1bx4bAND0UGABAM5LdHS0SktL9d133yk/P9/dgtWqVStFRkYqOztbubm5uuuuu+o8hp+f8cfRb83Md/qYq9/av1mzZu7Pp7okPv/882e0QoWEhGjdunWGYzzd2SbzOHWuU//3JH8AQNNGF0EAwHkJCAjQZZddpi+//FIdO3ZUSEiIe1uPHj20ZcsW2Wy2c7Yk1aVDhw46fvy4CgoK3Ovy8vLOuf/pXeucTqd++OEHQ+dq27atfHx89MsvvygiIkIREREKCQnRkiVLdPjwYUVERKioqMjdrU+SVq1apXnz5p3zuO3atVNRUZFKS0vd63JycuTj41Or5Q4AYA0UWACA8xYTE6Nvv/3WPQ37Kd27d9e2bdvOOf7qXNq3by+73a758+crLy9PW7du1RdffFHn/kOGDFFeXp4++OADFRQUaMmSJTp69KihcwUFBWnIkCF6++23lZmZqUOHDmnu3Ln64YcfFBkZqZ49e6p169ZKTk5Wfn6+du7cqTVr1ri7PgYGBurQoUO1CilJio2NVWRkpF5//XX95z//UVZWlhYvXqyEhAS1aNHC478TAEDTRoEFADhv3bt314kTJ9zdA0/p0aOHTpw4cUbh5YlHH31ULVu21NSpU/Xee+/p+uuvr3PfyMhIPfXUU0pNTdXkyZN17Ngx9ezZ0/C57rnnHsXHx2vOnDl65plnVFVVpWeffVb+/v7y8fHR5MmTVVpaqqeeekoLFy7UrbfeqoSEBEnSsGHDtHbtWr355pu1jnnqz9lsNj377LOaM2eOrrjiCk2cOLF+fyEAgCbN5vp1Z3UAAAAAQL3QggUAAAAADYQCCwAAAAAaCAUWAAAAADQQCiwAAAAAaCAUWAAAAADQQCiwAAAAAKCBUGABAAAAQAOhwAIAAACABvL/Ado2eh71WTUHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wind_dir = data[['wd','PM2.5']]\n",
    "wind_dir = wind_dir.groupby('wd').median()\n",
    "\n",
    "# plot the data\n",
    "with plt.style.context('ggplot'):\n",
    "    wind_dir.plot(figsize=(12,5),legend=False,kind='bar',linewidth=.9)\n",
    "    plt.xlabel('Wind direction',fontsize=14)\n",
    "    plt.ylabel('PM2.5 concentration (ug/m^3)',fontsize=14)\n",
    "    plt.title('Average hourly recorded PM2.5 concentration in the air in Aotizhongxin-Beijin\\ngrouped by wind direction',fontsize=16)\n",
    "    plt.grid(axis='x')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5><b>How do the other environmental factors affect the amount of PM2.5 concentration in the air ?</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's try and visualize the relationships between the features of the data\n",
    "plt.figure(figsize=(13,9))\n",
    "correlation_data = data[['PM2.5', 'PM10', 'SO2', 'NO2',\n",
    "                         'CO', 'O3', 'TEMP', 'PRES',\n",
    "                         'DEWP', 'RAIN', 'WSPM']]\n",
    "sns.heatmap(correlation_data.corr(),cmap=plt.cm.Reds,annot=True)\n",
    "plt.title('Heatmap displaying the correlation matrix of the variables',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><b>Model Training and Evaluation</b></h3>\n",
    "<br>\n",
    "<h5><b>check for multicollinearity among variables and fit a regression model using statsmodels</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    35340.419725\n",
       "PM2.5        6.350322\n",
       "PM10         4.811566\n",
       "SO2          1.670510\n",
       "NO2          3.320432\n",
       "CO           3.159039\n",
       "O3           2.250103\n",
       "TEMP         8.214959\n",
       "PRES         3.667310\n",
       "DEWP         6.517281\n",
       "RAIN         1.023639\n",
       "WSPM         1.788731\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_drop = ['date','year','month','day','hour','wd']\n",
    "newdata = data.drop(cols_to_drop,axis=1)\n",
    "\n",
    "# calculate the variance inflation factor of each feature and detect multicollinearity\n",
    "cons_data = sm.tools.add_constant(newdata)\n",
    "series_before = pd.Series([variance_inflation_factor(cons_data.values,i) for i in range(cons_data.shape[1])],\n",
    "                         index=cons_data.columns)\n",
    "series_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    33490.920665\n",
       "PM2.5        5.972024\n",
       "PM10         4.704558\n",
       "SO2          1.560019\n",
       "NO2          3.314615\n",
       "CO           3.133916\n",
       "O3           2.168322\n",
       "TEMP         4.559340\n",
       "PRES         3.463731\n",
       "RAIN         1.009170\n",
       "WSPM         1.460474\n",
       "dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can see that TEMP (temperature) and DEWP (dewpoint) are highly correlated as the VIF value is \n",
    "# greater than 5. As a result, we get rid of one of those features and probably the one that has the \n",
    "# lowest correlation with the dependent variable.\n",
    "newdata = newdata.drop('DEWP',axis=1)\n",
    "cons_data2 = sm.tools.add_constant(newdata)\n",
    "series_after = pd.Series([variance_inflation_factor(cons_data2.values,i) for i in range(cons_data2.shape[1])],\n",
    "                         index=cons_data2.columns)\n",
    "series_after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata.columns = ['PM2_5','PM10','SO2','NO2','CO','O3','TEMP','PRES','RAIN','WSPM']\n",
    "\n",
    "# PM2.5 is skewed to the right so we log transform the values to normalize the distribution\n",
    "newdata['PM2_5'] = np.log(newdata['PM2_5'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>PM2_5</td>      <th>  R-squared:         </th> <td>   0.712</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.712</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   9636.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 29 Feb 2020</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:28:20</td>     <th>  Log-Likelihood:    </th> <td> -30906.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 35064</td>      <th>  AIC:               </th> <td>6.183e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 35054</td>      <th>  BIC:               </th> <td>6.192e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    5.5181</td> <td>    0.571</td> <td>    9.668</td> <td> 0.000</td> <td>    4.399</td> <td>    6.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PM10</th>      <td>    0.0056</td> <td> 5.11e-05</td> <td>  109.188</td> <td> 0.000</td> <td>    0.005</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SO2</th>       <td>    0.0040</td> <td>    0.000</td> <td>   23.039</td> <td> 0.000</td> <td>    0.004</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NO2</th>       <td>    0.0060</td> <td>    0.000</td> <td>   39.113</td> <td> 0.000</td> <td>    0.006</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CO</th>        <td>    0.0002</td> <td>  4.2e-06</td> <td>   40.083</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>O3</th>        <td>    0.0021</td> <td>  8.1e-05</td> <td>   25.491</td> <td> 0.000</td> <td>    0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TEMP</th>      <td>    0.0110</td> <td>    0.001</td> <td>   18.899</td> <td> 0.000</td> <td>    0.010</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PRES</th>      <td>   -0.0028</td> <td>    0.001</td> <td>   -5.102</td> <td> 0.000</td> <td>   -0.004</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RAIN</th>      <td>    0.0237</td> <td>    0.003</td> <td>    6.888</td> <td> 0.000</td> <td>    0.017</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WSPM</th>      <td>   -0.1399</td> <td>    0.003</td> <td>  -45.047</td> <td> 0.000</td> <td>   -0.146</td> <td>   -0.134</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3697.836</td> <th>  Durbin-Watson:     </th> <td>   0.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5978.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td>-0.761</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 4.333</td>  <th>  Cond. No.          </th> <td>3.50e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.5e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  PM2_5   R-squared:                       0.712\n",
       "Model:                            OLS   Adj. R-squared:                  0.712\n",
       "Method:                 Least Squares   F-statistic:                     9636.\n",
       "Date:                Sat, 29 Feb 2020   Prob (F-statistic):               0.00\n",
       "Time:                        21:28:20   Log-Likelihood:                -30906.\n",
       "No. Observations:               35064   AIC:                         6.183e+04\n",
       "Df Residuals:                   35054   BIC:                         6.192e+04\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      5.5181      0.571      9.668      0.000       4.399       6.637\n",
       "PM10           0.0056   5.11e-05    109.188      0.000       0.005       0.006\n",
       "SO2            0.0040      0.000     23.039      0.000       0.004       0.004\n",
       "NO2            0.0060      0.000     39.113      0.000       0.006       0.006\n",
       "CO             0.0002    4.2e-06     40.083      0.000       0.000       0.000\n",
       "O3             0.0021    8.1e-05     25.491      0.000       0.002       0.002\n",
       "TEMP           0.0110      0.001     18.899      0.000       0.010       0.012\n",
       "PRES          -0.0028      0.001     -5.102      0.000      -0.004      -0.002\n",
       "RAIN           0.0237      0.003      6.888      0.000       0.017       0.030\n",
       "WSPM          -0.1399      0.003    -45.047      0.000      -0.146      -0.134\n",
       "==============================================================================\n",
       "Omnibus:                     3697.836   Durbin-Watson:                   0.373\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5978.516\n",
       "Skew:                          -0.761   Prob(JB):                         0.00\n",
       "Kurtosis:                       4.333   Cond. No.                     3.50e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.5e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the regression model\n",
    "mul_reg = formula.ols(formula='PM2_5 ~ PM10 + SO2 + NO2 + CO + O3 + TEMP + PRES + RAIN + WSPM',\n",
    "                      data=newdata).fit()\n",
    "mul_reg.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The OLS model from statsmodels gives us an accuracy of 71% (0.712) which is not satisfactory for prediction. So we move on to fit a linear regression model from the scikit-learn library.</p>\n",
    "<br>\n",
    "<h5><b>fitting a linear regression model with sklearn.linear_model.LinearRegression()</b></h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PM10</th>\n",
       "      <th>SO2</th>\n",
       "      <th>NO2</th>\n",
       "      <th>CO</th>\n",
       "      <th>O3</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>RAIN</th>\n",
       "      <th>WSPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.125409</td>\n",
       "      <td>-0.594053</td>\n",
       "      <td>-1.430285</td>\n",
       "      <td>-0.809141</td>\n",
       "      <td>0.365570</td>\n",
       "      <td>-1.253510</td>\n",
       "      <td>1.072316</td>\n",
       "      <td>-0.074107</td>\n",
       "      <td>2.235814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.082965</td>\n",
       "      <td>-0.594053</td>\n",
       "      <td>-1.430285</td>\n",
       "      <td>-0.809141</td>\n",
       "      <td>0.365570</td>\n",
       "      <td>-1.288611</td>\n",
       "      <td>1.091545</td>\n",
       "      <td>-0.074107</td>\n",
       "      <td>2.485022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.093576</td>\n",
       "      <td>-0.549641</td>\n",
       "      <td>-1.348251</td>\n",
       "      <td>-0.809141</td>\n",
       "      <td>0.294746</td>\n",
       "      <td>-1.288611</td>\n",
       "      <td>1.120388</td>\n",
       "      <td>-0.074107</td>\n",
       "      <td>3.232647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.104187</td>\n",
       "      <td>-0.283168</td>\n",
       "      <td>-1.320906</td>\n",
       "      <td>-0.809141</td>\n",
       "      <td>0.277040</td>\n",
       "      <td>-1.314937</td>\n",
       "      <td>1.216533</td>\n",
       "      <td>-0.074107</td>\n",
       "      <td>1.155913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.136020</td>\n",
       "      <td>-0.238756</td>\n",
       "      <td>-1.293562</td>\n",
       "      <td>-0.809141</td>\n",
       "      <td>0.277040</td>\n",
       "      <td>-1.367589</td>\n",
       "      <td>1.283835</td>\n",
       "      <td>-0.074107</td>\n",
       "      <td>0.242150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PM10       SO2       NO2        CO        O3      TEMP      PRES  \\\n",
       "0 -1.125409 -0.594053 -1.430285 -0.809141  0.365570 -1.253510  1.072316   \n",
       "1 -1.082965 -0.594053 -1.430285 -0.809141  0.365570 -1.288611  1.091545   \n",
       "2 -1.093576 -0.549641 -1.348251 -0.809141  0.294746 -1.288611  1.120388   \n",
       "3 -1.104187 -0.283168 -1.320906 -0.809141  0.277040 -1.314937  1.216533   \n",
       "4 -1.136020 -0.238756 -1.293562 -0.809141  0.277040 -1.367589  1.283835   \n",
       "\n",
       "       RAIN      WSPM  \n",
       "0 -0.074107  2.235814  \n",
       "1 -0.074107  2.485022  \n",
       "2 -0.074107  3.232647  \n",
       "3 -0.074107  1.155913  \n",
       "4 -0.074107  0.242150  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we split the data into predictor variables and Outcome variable\n",
    "X = newdata.drop('PM2_5',axis=1)\n",
    "y = newdata['PM2_5']\n",
    "\n",
    "# we need to scale or normalize the predictor variables since they are not on the same\n",
    "# scale and some of their distributions are skewed.\n",
    "X_scaled =  preprocessing.scale(X)\n",
    "X_scaled = pd.DataFrame(X_scaled,columns=X.columns)\n",
    "X_scaled.dropna(inplace=True)\n",
    "# print the scaled predictor variables.\n",
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we now split out data into train and test data\n",
    "X_train,X_test,y_train,y_test = train_test_split(X_scaled,y,test_size=.2,random_state=0)\n",
    "\n",
    "# instantiate the linear regression model\n",
    "lin_model = LinearRegression()\n",
    "lin_model.fit(X_train,y_train)   # fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on train data: 0.7116905619118638\n",
      "\n",
      "Score on test data: 0.7138206964831479\n"
     ]
    }
   ],
   "source": [
    "# we now score the model\n",
    "print('Score on train data: {}\\n'.format(lin_model.score(X_train,y_train)))\n",
    "print('Score on test data: {}'.format(lin_model.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.3384419958089878\n",
      "\n",
      "Overall model accuracy: 0.7138206964831479\n"
     ]
    }
   ],
   "source": [
    "prediction = lin_model.predict(X_test)\n",
    "mse = mean_squared_error(y_test,prediction)\n",
    "accuracy = r2_score(y_test,prediction)\n",
    "\n",
    "print('Mean Squared Error: {}\\n'.format(mse))\n",
    "print('Overall model accuracy: {}'.format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The model accuracy for the LinearRegression() is no better than that of the statsmodels. They all give the same accuracy is not better for making predictions. We now move on to fit other models by using the ensemble methods</p>\n",
    "<br>\n",
    "<br>\n",
    "<h3><b>Ensemble methods</b></h3>\n",
    "<p>For ensemble methods (DecisionTreeRegressor,RandomForestRegressor,and GradientBoostingRegressor),we include the pressure and rain features and we won't scale the predictor variables,neither would we log transform the outcome variable. At this part, model fitting and hyper-parameter tunning will be done at the same time. Instead of fitting the model with single parameters, we will straight away perform the grid search with multiple values for a parameter and find the best parameters for fitting the model on our data to get a satisfactory accuracy.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "ensemble_data = data.drop(cols_to_drop,axis=1)\n",
    "\n",
    "# we split the data into predictor variables and Outcome variable\n",
    "X = ensemble_data.drop('PM2.5',axis=1)\n",
    "y = ensemble_data['PM2.5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DecisionTreeRegressor\n",
    "we will now fit a decision tree regression model on the data and tune some of its parameters to increase the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features='auto',\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=2,\n",
       "                      min_samples_split=3, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we go ahead to use the ensemble methods as the LinearRegression model has a low accuracy\n",
    "# on both the test and train data.\n",
    "decision_tree = DecisionTreeRegressor(max_depth=5,\n",
    "                                     max_features='auto',\n",
    "                                     min_samples_split=3,\n",
    "                                     min_samples_leaf=2)\n",
    "decision_tree.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on train data: 0.8726415765577269\n",
      "\n",
      "Score on test data: 0.8585663434593064\n",
      "\n",
      "Root Mean Squared Error: 30.249022611467236\n",
      "\n",
      "Overall model accuracy: 0.8585663434593064\n"
     ]
    }
   ],
   "source": [
    "# we now score the model\n",
    "print('Score on train data: {}\\n'.format(decision_tree.score(xtrain,ytrain)))\n",
    "print('Score on test data: {}\\n'.format(decision_tree.score(xtest,ytest)))\n",
    "\n",
    "tree_pred = decision_tree.predict(xtest)\n",
    "tree_mse = mean_squared_error(ytest,tree_pred)\n",
    "tree_accuracy = r2_score(ytest,tree_pred)\n",
    "\n",
    "print('Root Mean Squared Error: {}\\n'.format(np.sqrt(tree_mse)))\n",
    "print('Overall model accuracy: {}'.format(tree_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=DecisionTreeRegressor(criterion='mse', max_depth=None,\n",
       "                                             max_features=None,\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             presort=False, random_state=None,\n",
       "                                             splitter='best'),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'max_depth': [3, 4, 5, 6, 7],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'min_samples_leaf': [2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_samples_split': [2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We now tune the parameters of the model to see if we can increase the accuracy\n",
    "params = {'max_depth':[3,4,5,6,7],\n",
    "         'max_features':['auto','sqrt','log2'],\n",
    "         'min_samples_split':[2,3,4,5,6,7,8,9,10],\n",
    "         'min_samples_leaf':[2,3,4,5,6,7,8,9,10]}\n",
    "\n",
    "tree = DecisionTreeRegressor()\n",
    "\n",
    "# initialize the grid search for the best parameters\n",
    "tree_search = GridSearchCV(tree,param_grid=params,\n",
    "                          n_jobs=-1,cv=5)\n",
    "\n",
    "tree_search.fit(xtrain,ytrain)   # fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on train data: 0.9028652194161406\n",
      "\n",
      "Score on test data: 0.888680893391384\n",
      "\n",
      "Best parameters found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'max_depth': 7,\n",
       " 'max_features': 'auto',\n",
       " 'min_samples_leaf': 8,\n",
       " 'min_samples_split': 2}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 26.83612572970069\n",
      "\n",
      "Overall model accuracy: 0.8886808933913839\n"
     ]
    }
   ],
   "source": [
    "# we now score the model\n",
    "print('Score on train data: {}\\n'.format(tree_search.score(xtrain,ytrain)))\n",
    "print('Score on test data: {}\\n'.format(tree_search.score(xtest,ytest)))\n",
    "print('Best parameters found:')\n",
    "display(tree_search.best_params_)\n",
    "\n",
    "tree_search_pred = tree_search.predict(xtest)\n",
    "tree_search_mse = mean_squared_error(ytest,tree_search_pred)\n",
    "tree_search_accuracy = r2_score(ytest,tree_search_pred)\n",
    "\n",
    "print('Root Mean Squared Error: {}\\n'.format(np.sqrt(tree_search_mse)))\n",
    "print('Overall model accuracy: {}'.format(tree_search_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestRegressor\n",
    "\n",
    "we now fit a random forest regression model on the data to see if we would get a better accuracy results than that of the decision tree regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=7,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=3, min_samples_split=7,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate the RandomForestRegressor model and fit the model on the training data\n",
    "forest = RandomForestRegressor(n_estimators=100,\n",
    "                              max_depth=7,\n",
    "                              max_features='auto',\n",
    "                              min_samples_split=7,\n",
    "                              min_samples_leaf=3)\n",
    "\n",
    "forest.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on train data: 0.9149654135707646\n",
      "\n",
      "Score on test data: 0.9045468853679719\n",
      "\n",
      "Root Mean Squared Error: 24.850207175331573\n",
      "\n",
      "Overall model accuracy: 0.9045468853679719\n"
     ]
    }
   ],
   "source": [
    "# we now score the model\n",
    "print('Score on train data: {}\\n'.format(forest.score(xtrain,ytrain)))\n",
    "print('Score on test data: {}\\n'.format(forest.score(xtest,ytest)))\n",
    "\n",
    "forest_pred = forest.predict(xtest)\n",
    "forest_mse = mean_squared_error(ytest,forest_pred)\n",
    "forest_accuracy = r2_score(ytest,forest_pred)\n",
    "\n",
    "print('Root Mean Squared Error: {}\\n'.format(np.sqrt(forest_mse)))\n",
    "print('Overall model accuracy: {}'.format(forest_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:  3.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestRegressor(bootstrap=True,\n",
       "                                                   criterion='mse',\n",
       "                                                   max_depth=None,\n",
       "                                                   max_features='auto',\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators='warn',\n",
       "                                                   n_jobs=None, oob_score=False,\n",
       "                                                   random_sta...\n",
       "                                                   warm_start=False),\n",
       "                   iid='warn', n_iter=10, n_jobs=-1,\n",
       "                   param_distributions={'max_depth': [3, 4, 5, 6, 7],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': [2, 3, 4, 5, 6, 7,\n",
       "                                                             8, 9, 10],\n",
       "                                        'min_samples_split': [2, 3, 4, 5, 6, 7,\n",
       "                                                              8, 9, 10],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we now tune the parameters of the RandomForestRegressor model using RandomizedSearchCV to \n",
    "# find the best parameters and increase the accuracy of the model\n",
    "\n",
    "params['n_estimators'] = [100,200,300,400,500]\n",
    "\n",
    "# instantiate the model\n",
    "random_forest = RandomForestRegressor()\n",
    "\n",
    "# perform the grid search for the best parameters\n",
    "forest_search = RandomizedSearchCV(random_forest,params,n_jobs=-1,\n",
    "                                   cv=5,verbose=2)\n",
    "forest_search.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on train data: 0.8988326176595388\n",
      "\n",
      "Score on test data: 0.8995933109458588\n",
      "\n",
      "Best parameters found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500,\n",
       " 'min_samples_split': 8,\n",
       " 'min_samples_leaf': 10,\n",
       " 'max_features': 'log2',\n",
       " 'max_depth': 7}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 25.486857158527293\n",
      "\n",
      "Overall model accuracy: 0.8995933109458589\n"
     ]
    }
   ],
   "source": [
    "# we now score the model\n",
    "print('Score on train data: {}\\n'.format(forest_search.score(xtrain,ytrain)))\n",
    "print('Score on test data: {}\\n'.format(forest_search.score(xtest,ytest)))\n",
    "print('Best parameters found:')\n",
    "display(forest_search.best_params_)\n",
    "\n",
    "forest_search_pred = forest_search.predict(xtest)\n",
    "forest_search_mse = mean_squared_error(ytest,forest_search_pred)\n",
    "forest_search_accuracy = r2_score(ytest,forest_search_pred)\n",
    "\n",
    "print('Root Mean Squared Error: {}\\n'.format(np.sqrt(forest_search_mse)))\n",
    "print('Overall model accuracy: {}'.format(forest_search_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingRegressor\n",
    "\n",
    "we now fit a gradient boosting regression model on the data to see if we would get a better accuracy results than that of the decision tree and random forest regression model and also minimize the error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "                          learning_rate=0.1, loss='ls', max_depth=7,\n",
       "                          max_features='auto', max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=3, min_samples_split=7,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                          n_iter_no_change=None, presort='auto',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate the GradientBoostingRegressor model and fit the model on the training data\n",
    "grad_boost = GradientBoostingRegressor(n_estimators=100,\n",
    "                                      max_depth=7,\n",
    "                                      max_features='auto',\n",
    "                                      min_samples_split=7,\n",
    "                                      min_samples_leaf=3,\n",
    "                                      learning_rate=0.1)\n",
    "\n",
    "grad_boost.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on train data: 0.9629363030230438\n",
      "\n",
      "Score on test data: 0.9343132063362775\n",
      "\n",
      "Root Mean Squared Error: 20.614556387477222\n",
      "\n",
      "Overall model accuracy: 0.9343132063362775\n"
     ]
    }
   ],
   "source": [
    "# we now score the model\n",
    "print('Score on train data: {}\\n'.format(grad_boost.score(xtrain,ytrain)))\n",
    "print('Score on test data: {}\\n'.format(grad_boost.score(xtest,ytest)))\n",
    "\n",
    "gboost_pred = grad_boost.predict(xtest)\n",
    "gboost_mse = mean_squared_error(ytest,gboost_pred)\n",
    "gboost_accuracy = r2_score(ytest,gboost_pred)\n",
    "\n",
    "print('Root Mean Squared Error: {}\\n'.format(np.sqrt(gboost_mse)))\n",
    "print('Overall model accuracy: {}'.format(gboost_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:  2.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "                   estimator=GradientBoostingRegressor(alpha=0.9,\n",
       "                                                       criterion='friedman_mse',\n",
       "                                                       init=None,\n",
       "                                                       learning_rate=0.1,\n",
       "                                                       loss='ls', max_depth=3,\n",
       "                                                       max_features=None,\n",
       "                                                       max_leaf_nodes=None,\n",
       "                                                       min_impurity_decrease=0.0,\n",
       "                                                       min_impurity_split=None,\n",
       "                                                       min_samples_leaf=1,\n",
       "                                                       min_samples_split=2,\n",
       "                                                       min_weight_fraction_leaf=0.0,\n",
       "                                                       n_estimators=100,...\n",
       "                   param_distributions={'learning_rate': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       "                                        'max_depth': [3, 4, 5, 6, 7],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': [2, 3, 4, 5, 6, 7,\n",
       "                                                             8, 9, 10],\n",
       "                                        'min_samples_split': [2, 3, 4, 5, 6, 7,\n",
       "                                                              8, 9, 10],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=2)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we now tune the parameters of the GradientBoostingRegressor model using RandomizedSearchCV to \n",
    "# find the best parameters and increase the accuracy of the model\n",
    "\n",
    "params['learning_rate'] = np.linspace(0.1,1,10)\n",
    "\n",
    "# instantiate the model\n",
    "gradient_boosting = GradientBoostingRegressor()\n",
    "\n",
    "# perform the grid search for the best parameters\n",
    "gboost_search = RandomizedSearchCV(gradient_boosting,params,n_jobs=-1,\n",
    "                                   cv=5,verbose=2)\n",
    "gboost_search.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on train data: 0.9537995646052737\n",
      "\n",
      "Score on test data: 0.9304405766853177\n",
      "\n",
      "Best parameters found:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 400,\n",
       " 'min_samples_split': 9,\n",
       " 'min_samples_leaf': 10,\n",
       " 'max_features': 'log2',\n",
       " 'max_depth': 5,\n",
       " 'learning_rate': 0.1}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error: 21.213530276613525\n",
      "\n",
      "Overall model accuracy: 0.9304405766853177\n"
     ]
    }
   ],
   "source": [
    "# we now score the model\n",
    "print('Score on train data: {}\\n'.format(gboost_search.score(xtrain,ytrain)))\n",
    "print('Score on test data: {}\\n'.format(gboost_search.score(xtest,ytest)))\n",
    "print('Best parameters found:')\n",
    "display(gboost_search.best_params_)\n",
    "\n",
    "gboost_search_pred = gboost_search.predict(xtest)\n",
    "gboost_search_mse = mean_squared_error(ytest,gboost_search_pred)\n",
    "gboost_search_accuracy = r2_score(ytest,gboost_search_pred)\n",
    "\n",
    "print('Root Mean Squared Error: {}\\n'.format(np.sqrt(gboost_search_mse)))\n",
    "print('Overall model accuracy: {}'.format(gboost_search_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we now use the best model (GradientBoostingRegressor model) to predict the PM2.5 \n",
    "# concetration and compare it to the actual PM2.5 recorded in the data by means of\n",
    "# visualization\n",
    "\n",
    "compare_data = pd.DataFrame({'dates':data['date'],\n",
    "                            'Actual PM2.5':y,\n",
    "                            'Predicted PM2.5':gboost_search.predict(X.values)})\n",
    "\n",
    "compare_data.set_index('dates',inplace=True)\n",
    "compare_data['Predicted PM2.5'] = np.round(compare_data['Predicted PM2.5'],1)\n",
    "\n",
    "# let's plot the daily averages of the Actual PM10 and the predicted PM2.5 concentration.\n",
    "compare_data = compare_data.resample('D').mean()\n",
    "\n",
    "with plt.style.context('fivethirtyeight'):\n",
    "    plt.figure(figsize=(12,5))\n",
    "    plt.scatter(compare_data.index,compare_data['Actual PM2.5'],s=15,label='Actual PM2.5',\n",
    "               alpha=.6)\n",
    "    plt.scatter(compare_data.index,compare_data['Predicted PM2.5'],s=15,label='Predicted PM2.5',\n",
    "               alpha=.6)\n",
    "    plt.legend()\n",
    "    plt.title('Evaluating the GradientBoostingRegressor model\\n(model accuracy = 93%)',\n",
    "             fontsize=18)\n",
    "    plt.xlabel('period',fontsize=15)\n",
    "    plt.ylabel('PM2.5 concentration',fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5><b>Residuals analysis</b></h5>\n",
    "<p>Now that we have successfully trained a regression model that predicts the amount of PM2.5 concetration in the air with a 93% accuracy given other environmental features, we have to analyze the errors of prediction to see if the model satisfies the regression errors asumption. That is,the errors must be normally distributed and do not follow any pattern.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the errors\n",
    "compare_data['Residuals'] = compare_data['Actual PM2.5'] - compare_data['Predicted PM2.5']\n",
    "\n",
    "# make a scatter plot of the errors to see if they follow any pattern\n",
    "with plt.style.context('ggplot'):\n",
    "    plt.figure(figsize=(12,5))\n",
    "    plt.scatter(compare_data.index,compare_data.Residuals,alpha=.7)\n",
    "    plt.title('Residual scatter plot',fontsize=16)\n",
    "    plt.ylabel('Errors',fontsize=15)\n",
    "    plt.grid(axis='x')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the histogram to see check the normality of the errors\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.distplot(compare_data.Residuals,bins=50)\n",
    "plt.title('Distribution of the residuals from the gradient boosting model',\n",
    "         fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>As seen above, the residuals of the predictions follow no pattern and also have a normal distribution which satisfies the regression error assumptions. This proves that our model is accurate and good for further predictions.</p>\n",
    "<br>\n",
    "<h3><b>Saving and loading the model</b></h3>\n",
    "<p>Now that the model has been successfully trained, the next thing is to save and be able to load it anytime we want to use it. To do this, we have to save it as a pickle file using the joblib module from sklearn.externals. The cells below shows you how to save your trained machine learning model and also load it anytime you want.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model to my desktop\n",
    "joblib.dump(gboost_search,'.\\\\Desktop\\\\pm25_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the model\n",
    "regression_model = joblib.load('.\\\\Desktop\\\\pm25_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<h4><b>Project completed by: Prince Owusu</b></h4>\n",
    "<p><a href=\"mailto:powusu381@gmail.com\" target=\"_blank\">Email</a> || <a href=\"https://www.linkedin.com/in/prince-owusu-356914198?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3B2NYoXqMHQKOMp0yWSME5mQ%3D%3D\" target=\"_blank\">LinkedIn</a> || <a href=\"https://twitter.com/iam_kwekhu\" target=\"_blank\">Twitter</a></p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
